ENHANCING BIOFILTRATION TECHNOLOGY FOR THE TREATMENT OF
WILDFIRE-IMPACTED RAW WATER
by
Adedamola Adesomi Ali
B.Eng., University of Johannesburg, 2021

THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF APPLIED SCIENCE
IN
ENGINEERING

UNIVERSITY OF NORTHERN BRITISH COLUMBIA
August 2025
© Adedamola Adesomi Ali, 2025

Abstract
Wildfire events significantly contribute to the degradation of surface water quality by
introducing ashes and unburnt carbon through runoff, resulting in elevated turbidity, increased
levels of total suspended and dissolved solids, and higher concentrations of dissolved organic
matter (DOM). Biofiltration has emerged as a sustainable and cost-effective water treatment
technology, particularly valued for its environmentally friendly ability to remove DOM. This study
evaluated the performance of process intensified biofiltration in removing dissolved organic
carbon (DOC) from wildfire-impacted raw water using three bench-scale biofilters composed of
different media: sand, granular activated carbon (GAC), and a combination of sand and GAC. The
influent was pretreated using aeration and a roughing filter. DOC was tested at three levels: 20,
50, and 100 mg/L. Water quality parameters such as pH, turbidity, temperature, alkalinity,
ultraviolet absorbance at 254 nm (UVA₅₂₄), specific ultraviolet absorbance at 254nm (SUVA₅₂₄) –
calculated, and microbial activity – adenosine triphosphate (ATP), extracellular polymeric
substances (EPS), and dissolved oxygen (DO) were monitored. Results indicate that at 20 mg/L
DOC, the sand + GAC biofilter achieved the highest mean DOC removal efficiency of 70 ± 20%,
followed by GAC (68 ± 16%) and sand (68 ± 22%). At 50 mg/L, removal efficiencies declined
to 54 ± 16% (sand + GAC), 47 ± 19% (GAC), and 33 ± 19% (sand). At 100 mg/L, performance
dropped remarkably, with average removals of only 16 ± 26% (sand + GAC), 16 ± 21% (GAC),
and 12 ± 22% (sand). UVA₅₂₄ and SUVA₅₂₄ values remained below 2 L/mg·m, indicating the
dominance of hydrophilic and low molecular weight organic matter. ATP concentrations peaked
at 14 ± 2 mM in the sand biofilter, confirming its superior microbial activity. EPS analysis revealed
higher sugar than protein concentrations, with tightly bound EPS peaking at 296 ± 37 mg/g total
suspended solids. Elevated influent DOC concentrations were associated with increased pH (up

ii

to 9.52 ± 0.12), which may have negatively impacted microbial activity and DOC removal.
Statistical analysis revealed a strong negative correlation between DOC concentration and DOC
removal efficiency. Furthermore, analysis of variance confirmed that DOC concentration
significantly influenced removal efficiency, while media type did not. However, Tukey post-hoc
test showed that at DOC concentrations of 20 and 50 mg/L, the biofilters significantly reduced
DOC levels, while at 100 mg/L, no significant reduction was observed, indicating diminished
effectiveness at higher concentrations. The findings of this study underscore the robustness and
adaptability of biofiltration systems in treating wildfire-impacted water, particularly in the
effective removal of DOC and turbidity. By providing a nature-based, cost-effective treatment
solution, biofiltration offers a sustainable and climate-resilient strategy for safeguarding drinking
water sources against emerging contaminants.

iii

Table of Contents
Abstract ........................................................................................................................................... ii
Table of Contents ........................................................................................................................... iv
List of Tables ................................................................................................................................. ix
List of Figures ................................................................................................................................. x
List of Equations ........................................................................................................................... xii
Acronyms and Abbreviations ...................................................................................................... xiii
Acknowledgements ....................................................................................................................... xv
Contributions................................................................................................................................ xvi
Chapter 1 Introduction .................................................................................................................... 1
1.1. Background .......................................................................................................................... 1
1.2. Research questions ............................................................................................................... 2
1.3. Research objectives .............................................................................................................. 3
1.4. Organization of the thesis .................................................................................................... 3
Chapter 2 Literature Review ........................................................................................................... 5
2.1. Synthesis of literature review on biofiltration application to water treatment..................... 5
2.1.1. Literature selection........................................................................................................ 5
2.1.2. Bibliometric overview .................................................................................................. 6
2.1.3. Presentation structure .................................................................................................... 8
2.2. Climate change and water .................................................................................................... 8

iv

2.2.1. Impacts of climate change on water quality ................................................................. 8
2.2.2. Impacts of climate change on water treatment ........................................................... 14
2.3. Drinking water treatment technologies resilient to climate change ................................... 17
2.3.1. Conventional filtration ................................................................................................ 17
2.3.2. Adsorption on activated carbon .................................................................................. 17
2.3.3. Coagulation and flocculation ...................................................................................... 18
2.3.4. Membrane processes ................................................................................................. 19
2.3.5. Advanced oxidation processes and disinfection ......................................................... 20
2.4. Biofiltration as a low-cost techno-ecological nature-based solution for water treatment . 21
2.4.1. Biofiltration as a water treatment technology ............................................................. 21
2.4.2. Pollutant removal mechanism in biofiltration during water treatment ....................... 22
2.4.3. Factors influencing biofiltration ................................................................................. 25
2.4.4. Dominant microbes in biofiltration............................................................................. 35
2.5. Resilience and adaptation of biofiltration to climate change ............................................. 36
2.6. Challenges of biofiltration ................................................................................................. 39
2.7. Summary of reviewed literature......................................................................................... 40
Chapter 3 Materials and Methods ................................................................................................. 43
3.1. Wildfire-impacted raw water preparation .......................................................................... 43
3.2. Pre-treatment ...................................................................................................................... 43
3.3. Biofilter design and operation ............................................................................................ 44
v

3.4. Experimental design........................................................................................................... 45
3.5. Analytical methods ............................................................................................................ 46
3.5.1. Dissolved oxygen (DO) and temperature ................................................................... 47
3.5.2. Turbidity...................................................................................................................... 47
3.5.3. pH................................................................................................................................ 47
3.5.4. Alkalinity..................................................................................................................... 48
3.5.5. Ultraviolet absorbance at 254 nm (UVA₅₂₄) ................................................................ 48
3.5.6. Extracellular polymeric substances (EPS) .................................................................. 48
3.5.7. Adenosine triphosphate (ATP) .................................................................................... 51
3.5.8. Biodegradable dissolved organic carbon (BDOC)...................................................... 52
3.5.9. Dissolved organic carbon (DOC) ................................................................................ 53
3.6. Statistical analyses ............................................................................................................. 55
Chapter 4 Results and Discussion ................................................................................................. 56
4.1. Influent water quality ......................................................................................................... 56
4.2. Temperature ....................................................................................................................... 56
4.3. Turbidity ............................................................................................................................ 57
4.4. pH....................................................................................................................................... 60
4.5. Alkalinity ........................................................................................................................... 63
4.6. Ultraviolet absorbance at 254 nm (UVA₅₂₄) and specific ultraviolet absorbance at 254nm
(SUVA₅₂₄) ................................................................................................................................. 65
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4.7. Dissolved Oxygen (DO) .................................................................................................... 68
4.8. Adenosine triphosphate (ATP) .......................................................................................... 69
4.9. Extracellular polymeric substances (EPS) ......................................................................... 71
4.10.

Biodegradable dissolved organic carbon (BDOC) and dissolved organic carbon

(DOC) profile ............................................................................................................................ 73
4.11. Statistical Analyses .......................................................................................................... 76
4.11.1.

Outlier test ............................................................................................................. 76

4.11.2.

Normality test........................................................................................................ 77

4.11.3.

Residual plots ........................................................................................................ 78

4.11.4.

Correlation test ...................................................................................................... 79

4.11.5.

Pareto chart ........................................................................................................... 80

4.11.6.

Analysis of variance (ANOVA) ............................................................................ 81

4.11.7. Tukey post-hoc test ................................................................................................... 82
Chapter 5 Conclusions and Recommendations............................................................................. 84
5.1. Conclusions ........................................................................................................................ 84
5.2. Limitations of the study ..................................................................................................... 85
5.3. Recommendations .............................................................................................................. 85
References ..................................................................................................................................... 87
Appendices .................................................................................................................................. - 1 Appendix A – Calibration curves ........................................................................................... - 1 vii

Appendix B – Influent water quality ...................................................................................... - 3 Appendix C – Experimental results ........................................................................................ - 5 Appendix D – Statistical Analyses ......................................................................................... - 8 Appendix E – Copyrights permissions ................................................................................... - 9 -

viii

List of Tables
Table 2.1: Trends of water quality parameters over the years. ..................................................... 12
Table 2.2: Summary of the impact of climate change on water quality and WTPs ..................... 16
Table 2.3: Characteristics of biofilter media................................................................................. 27
Table 3.1: The experimental runs based on biofilter media type and DOC concentration as the
factors. ........................................................................................................................................... 46
Table 4.1: Previously reported EPS concentration from water biofilters ..................................... 73
Table 4.2: BDOC concentration of influent water ........................................................................ 74
Table 4.3: Pairwise Spearman correlation .................................................................................... 80
Table 4.4: Analysis of variance .................................................................................................... 82
Table A.1: Comparison between DO and ATP concentration of all runs .................................... -7Table A.2: Regression equation .................................................................................................... -8Table A.3: Regression coefficients and model summary ............................................................. -8Table A.4: Fits and diagnostics for unusual observation .............................................................. -8-

ix

List of Figures
Figure 2.1: Flow chart for the overall procedure in literature selection. .........................................6
Figure 2.2: Number of peer-reviewed articles on biofiltration of drinking water since year 2000. 7
Figure 2.3: Mechanism of biofiltration systems. ...........................................................................23
Figure 3.1: Roughing filter set-up. .................................................................................................44
Figure 3.2: Full water treatment setup ...........................................................................................45
Figure 4.1: Mean temperature of influents and effluents at DOC = 20 mg/L (a) 50 mg/L (b), and
100 mg/L (c) ..................................................................................................................................57
Figure 4.2: Trends of turbidity reduction at DOC = 20 mg/L (a), 50 mg/L (b), and 100 mg/L (c)
........................................................................................................................................................59
Figure 4.3: Trends of pH of influents and effluents at DOC = 20 mg/L (a) 50 mg/L (b), and 100
mg/L (c) .........................................................................................................................................62
Figure 4.4: Trends of alkalinity of influents and effluents at DOC = 20 mg/L (a) 50 mg/L (b), and
100 mg/L (c) ..................................................................................................................................64
Figure 4.5: UVA trends for DOC at 20 mg/L (a), 50 mg/L (b), and 100 mg/L (c) .......................66
Figure 4.6: Mean SUVA of influents and effluents at DOC = 20 mg/L (a) 50 mg/L (b), and 100
mg/L (c) .........................................................................................................................................68
Figure 4.7: Mean DOC of influents and effluents at DOC = 20 mg/L (a) 50 mg/L (b), and 100
mg/L (c) .........................................................................................................................................69
Figure 4.8: ATP concentration at steady state (a) and average DO concentration (b) of all runs .70
Figure 4.9 EPS proteins (a), and sugar (b) concentration at each run ...........................................72
Figure 4.10:Average DOC removal (%) of influents and effluents at DOC = 20 mg/L (a) 50 mg/L
(b), and 100 mg/L (c) .....................................................................................................................74

x

Figure 4.11: Outlier plot for DOC removal at α = 0.05 (a) and 0.1 (b) .........................................77
Figure 4.12: Normality test results for the DOC removal efficiency among the biofiltration systems
........................................................................................................................................................78
Figure 4.13: Residual plots for DOC removal ...............................................................................79
Figure 4.14: Matrix plot of DOC concentration and DOC removal ..............................................80
Figure 4.15: Pareto chart of the standardized effects .....................................................................81
Figure 4.16: Interval plots of DOC removal vs media type at DOC = 20 mg/L (a) 50 mg/L (b), and
100 mg/L (c) ..................................................................................................................................83
Figure A.1: Calibration curve for COD solution .......................................................................... -1Figure A.2: ATP calibration curve for DOC at 20 mg/L (a), 50 mg/L (b), and 100 mg/L (c) ..... -1Figure A.3: Calibration curve for EPS - Proteins (a & b) and EPS - Sugar (c) ............................ -2Figure A.4: Influent water analysis report .................................................................................... -3Figure A.5: Influent water analysis report .................................................................................... -4Figure A.6: DOC concentration of influent and effluent at DOC = 20 mg/L (a), 50 mg/L (b), and
100 mg/L (c) ................................................................................................................................. -5Figure A.7: ATP concentration of biofilter media at DOC = 20 mg/L (a), 50 mg/L (b), and 100
mg/L (c) ........................................................................................................................................ -6Figure A.8: Journal permission ..................................................................................................... -9Figure A.9: Co-author’s permission ............................................................................................. -9Figure A.10: Co-author’s permission ......................................................................................... -10-

xi

List of Equations
Equation 3.1 .................................................................................................................................. 48
Equation 3.2 .................................................................................................................................. 53
Equation 3.3 .................................................................................................................................. 53
Equation 3.4 .................................................................................................................................. 53

xii

Acronyms and Abbreviations
ANOVA

Analysis of Variance

AO

Aesthetic Objectives

AOP

Advanced Oxidative Processes

ATP

Adenosine Triphosphate

BDOC

Biodegradable Dissolved Organic Carbon

BOM

Biodegradable Organic Matter

BSA

Bovine Albumin Serum

COD

Chemical Oxygen Demand

DBP

Disinfection By-Product

DO

Dissolved Oxygen

DOC

Dissolved Organic Carbon

DOM

Dissolved Organic Matter

EBCT

Empty Bed Contact Time

EFL

Enhanced Forestry Laboratory

EPS

Extracellular Polymeric Substances

GAC

Granular Activated Carbon

GCDWQ

Guidelines for Canadian Drinking Water Quality

HLR

Hydraulic Loading Rate

KS

Kolmogorov-Smirnov

MAC

Maximum Acceptable Concentration

N-DBP

Nitrogenous Disinfection By-products

NF

Nanofiltration

xiii

NOM

Natural Organic Matter

OG

Operational Guidelines

PPCPs

Pharmaceuticals and Personal Care Products

RO

reverse osmosis

SA

Sand-Anthracite

SSF

Slow Sand Filtration

TOC

Total Organic Carbon

TSS

Total Suspended Solids

SUVA₅₂₄)

Specific Ultraviolet Absorbance at 254 nm

UVA

Ultraviolet Absorbance

UVA₅₂₄

Ultraviolet Absorbance at 254 nm

UVR

Ultraviolet Radiation

WTP

Water Treatment Plant

xiv

Acknowledgements
I would like to express my deepest gratitude to my supervisors, Dr. Oliver Iorhemen and Dr.
Ronald Thring, for their invaluable guidance, support, and encouragement throughout this research
journey. Their expertise and insights have been instrumental in shaping this thesis. I am also
immensely grateful to my supervisory committee, Dr. Natalie Linklater, Dr. Jane Fowler, and Mr.
Hayat Raza, for their constructive feedback and unwavering support. Their diverse perspectives
and expertise have enriched my research.
A special thank you to the lab technicians, Doug Thompson and Kennedy Boateng, whose
technical assistance and dedication ensured the smooth execution of my experiments. Your hard
work and commitment have been crucial to the success of this project. I would like to extend my
heartfelt thanks to my research team, for their collaboration, support, and camaraderie. Working
with such a talented and dedicated group of individuals has been a truly rewarding experience. To
my friends and student life team, thank you for your constant encouragement and for being a source
of joy and motivation. Your support has been invaluable during the challenging times. Lastly, I
am profoundly grateful to my family for their unconditional love, patience, and support. Your
belief in me has been my greatest strength throughout this journey.

xv

Contributions
The following academic contributions emerged from this MASc work:
1. Ali, A.A., Iorhemen O.T., Thring R.W. (2025). Climate adaptation and resilience of
biofiltration as a low-cost technological solution for water treatment – A critical review.
Cleaner Water. 3: 100062.
2. Ali, A.A., Nabaterega, R., Thring, R.W., Iorhemen, O.T., Process-enhanced water
biofiltration for the treatment of wildfire-impacted raw water. in preparation.
Details of contributions from the candidate and co-authors are listed below:
1. In this publication, the candidate conducted extensive review of related literature on
climate change and water, water treatment technologies that are resilient to climate change,
biofiltration as low-cost techno-ecological nature-based solutions for water treatment, and
resilience and adaptation of biofiltration to climate change, manuscript writing, and
proofreading. Dr. Oliver Iorhemen provided significant contributions in terms of review
and editing during manuscript preparation. Dr. Ronald Thring provided significant
contributions in terms of technical guidance and review during manuscript preparation.
2. In this article, the candidate designed and performed all the experiments except the ones
stated below to have been conducted by others. The candidate also collected and analyzed
experimental data, wrote the first draft of the manuscript, revised the manuscript, and wrote
the final manuscript. Dr. Resty Nabaterega contributed to writing the first draft of the
manuscript, and proof reading, data analysis in Minitab software, research guidance,
scientific input and comments during manuscript write up. Dr. Oliver Iorhemen and Dr.
Ronald Thring contributed to research guidance, scientific input and comments during
manuscript write up, and proof reading.

xvi

Chapter 1 Introduction
1.1. Background
The quality and quantity of water are of vital significance for human existence. Both human
and environmental health have constantly been threatened by water pollution resulting from
increasing population, industrial development, and climate change [1], [2]. Over the years, climate
change has shown a direct effect on surface water quality. Extreme events such as wildfires, rising
temperatures, and changes in precipitation patterns pose concerning threats to drinking water.
Wildfires increase erosion, resulting in increased total suspended solids (TSS) and turbidity in
surface waters [3], [4], [5]. This increases the chance of unburnt organic matter transport from the
terrestrial ecosystem to nearby water bodies. The presence of unburnt organic matter in water
bodies results in increased concentrations of dissolved organic matter (DOM) in surface waters
[4]. Additionally, the increase in temperature and precipitation, posed by climate change, has
resulted in increased ultraviolet radiation (UVR) exposure to humans [6] and transportation of
contaminants into surface waters [3], [7]. The carcinogenic effects of UVR have resulted in the
frequent use of sunscreens to reduce the risk of skin cancer [8]. These sunscreens end up in our
surface water sources.
Current water treatment plants (WTPs) in low resource contexts struggle to remove these
elevated levels of DOM, turbidity, and pollutant load of the raw water sources, thereby increasing
energy costs and impairing efficiency of WTPs [9]. Despite the challenges caused by climate
change, biofiltration systems have a strong potential to increase resiliency. Biofiltration, a lowcost, nature-based technological solution, has emerged as a viable strategy for improving water
quality. This innovative water treatment technology makes use of natural processes to remove
pollutants present in various raw water sources. Biofiltration offers a cost-effective and
1

environmentally friendly approach by utilizing microorganisms. However, climate change
threatens the effectiveness of biofiltration, making it necessary to understand the resilience of this
sustainable solution to climate change.
Research has demonstrated that biofiltration offers a sustainable solution by effectively
removing dissolved organic carbon (DOC) and turbidity in wildfire-impacted water [6], [10], [11],
[12], [13], [14]. Blackburn et al. [6] explored the resilience of biofiltration to wildfire ashassociated organic carbon threat to potable water treatment using slow sand filtration (SSF) as a
biofilter operated at room temperature and an extended empty bed contact time (EBCT) of 10h.
Results showed an average of ~20% DOC removal and turbidity reduced to ≤0.3 NTU. However,
there has been limited research on the robustness of biofiltration for high DOC concentrations.
Therefore, the current study applied biofiltration to enhance its treatment effectiveness for
wildfire-impacted raw water at varying DOC concentrations.
1.2. Research questions
The proposed research sought to address two research questions focused on enhancing the
treatment performance of biofiltration in the face of a changing climate. These include:
a) How does media type (adsorbing and non-adsorbing) affect biofiltration performance?
b) What is the impact of varying DOC on the treatment performance of biofiltration?
Answering these questions provided insights into the optimal conditions for the operation of
biofiltration as a climate-adaptive and resilient water treatment technology.

2

1.3. Research objectives
The primary objective of this research was to enhance biofiltration, a nature-based treatment
system, for the treatment of wildfire-impacted water. To achieve this, the study focused on two
specific objectives:
a) To determine the impact of media type (adsorbing and non-adsorbing) on biofiltration
performance.
b) To determine the influence of varying DOC concentrations on the treatment performance
of biofiltration
These objectives provided actionable insights for enhancing biofiltration efficiency for the
treatment of wildfire-impacted water.
1.4. Organization of the thesis
This thesis is structured into five chapters. Chapter 1, the current chapter, introduces the
background, the impacts of climate change on water quality and water treatment technologies, the
resilience of biofiltration in treating wildfire-impacted raw water, discusses the research question
and highlights the objectives of the research.
Chapter 2 covers an in-depth literature review of the impacts of extreme weather events on
water quality and the operations of WTPs. Several conventional water treatment methods were
highlighted, and their limitations in treating emerging contaminants were discussed. The
mechanisms through which biofiltration removes contaminants as well as the key parameters that
influence biofiltration, such as biofilter media, types of microorganisms, temperature, pH, nutrient
supply, etc., and the dominant microbes present in biofilters were reviewed. The adaptation and
resilience of biofiltration systems to challenges posed by climate change in water treatment were

3

extensively discussed. Finally, the limitations and opportunities related to the adaptation and
resilience of biofiltration were discussed, emphasizing the need for more proactive measures to
optimize biofiltration systems
Chapter 3 outlines the experimental procedures for the treatment of wildfire-impacted raw
water using biofiltration. The chapter details the use several experimental runs to understand the
relationships between variables and their effects on DOC removal. In addition, analytical
procedures performed during the experimental period were discussed in full detail. Outlier and
normality tests, as well as Spearman’s correlation analysis were used to highlight the various
correlations between each factor and DOC removal efficiency. This combined approach aimed to
identify optimal conditions for wildfire-impacted raw water treatment for further optimization
studies.
Chapter 4 presents and interprets the experimental findings from this study. It begins with an
analysis of key water quality parameters, including dissolved oxygen, temperature, turbidity, pH,
and alkalinity, emphasizing their influence on biofilter performance. Organic matter characteristics
through ultraviolet absorbance at 254 nm (UVA₅₂₄), specific ultraviolet absorbance at 254nm
(SUVA₅₂₄), and DOC removal trends were explored, linking these to microbial activity and
treatment efficiency. Microbial dynamics are further examined using adenosine triphosphate
(ATP) concentrations and extracellular polymeric substances (EPS) composition. The discussion
integrates these findings with existing literature to contextualize the observed trends and
variations.
Chapter 5 summarizes the research findings on the impact of each experimental factor on
treating wildfire-impacted water. This chapter compares the previous studies biofiltration systems

4

for drinking water with this study, and it concludes with recommendations for future research
directions. The references supporting this thesis are listed at the end.

Chapter 2 Literature Review
2.1. Synthesis of literature review on biofiltration application to water treatment
2.1.1. Literature selection
A comprehensive search was conducted to identify peer-reviewed articles, technical reports,
conference papers, and relevant books that discuss biofiltration as a water treatment technology
and its resilience and adaptation to climate change. A systematic search on reputable academic
databases, including Google Scholar, Web of Science, ScienceDirect, Scopus, and Semantic
Scholar, was conducted for pertinent peer-reviewed journal publications, with additional data
obtained through forward citation elimination. The focus was on journal articles published within
the years 2000 – 2023. Keywords such as “bio-filtration”, “climate change”, “resilience of
biofiltration”, “water quality”, and “water treatment” were used within the "Topic" field. To
narrow the results down, Boolean operators were used in the search queries and the keywords
included “biofiltration AND water treatment”, “biofiltration of drinking water” “climate change
AND biofiltration” “climate change AND water quality OR water treatment plants”, “removal
mechanisms in biofiltration during water treatment”, “factors influencing biofiltration”, “resilience
AND biofiltration AND climate change”, “adaptation AND biofiltration AND climate change”,
“resilience of biofiltration to climate change”. The search was refined to include only Englishlanguage documents, peer-reviewed journal articles and scientific reports. Only journal articles
with relevant titles and abstracts were further reviewed if they discussed biofiltration as a water
treatment technology with a focus on its resilience and adaptation to climate change. 934 articles

5

were chosen for further analysis, among which 64 were review articles, 153 were proceeding
papers, and the rest were research articles (Figure 2.1). From this subset, 87 articles were
specifically cited for their relevance to biofiltration as a water treatment technology in the face of
climate change. Figure 2.1 shows the literature selection procedure on Web of Science.

General Search
4,278 articles were found using “biofiltration” in the title and abstract

Narrowing
934 articles were found using biofiltration and water treatment in the
title and abstract, excluding gas treatment. Year: 2000 – 2023,

Document type: Articles, Proceeding paper, Review articles, and
Book chapters

Final
766 articles, 153 Proceeding paper, 64 Review article, and 2 Book
chapters

Figure 2.1: Flow chart for the overall procedure in literature selection.
2.1.2. Bibliometric overview
A total of 934 documents were retrieved by focusing on biofiltration in water treatment within
24 years. The data extracted was grouped and arranged in a chronological order to identify the
6

trends and the annual growth of the publications from 2000 to 2023. Figure 2.2 shows the annual
publications of research articles on biofiltration for water treatment indexed in Web of Science in
the last 24 years. Based on Figure 2.2, the oldest publication on biofiltration for water treatment
began in 2000, and there was a total of 18 publications in that year. It clearly shows that the
research trend in the first six years (2000 – 2006) did not receive very high attention, as the annual
publications for each year are lower than 20. The total publications within 2000 – 2006 were 99,
accounting for only 10.5% of the total cumulative publications from the year 2000 to 2023.

140
115

Number of Articles

120
100
80

65

60

47

40

20

18

13

9

67
56 54

60 58

38 35

26 28 28
17 17 15

52

69

23
14

10

2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023

0

Year
Figure 2.2: Number of peer-reviewed articles on biofiltration of drinking water since year
2000.
The strong interest in biofiltration for water treatment started from 2007, as there was a
significant and rapid increase in the total publications from that year onwards. Between 2007 and
2014, the annual publications increased from 26 to 115 in 2014, which resulted in approximately
440% growth, the highest during this period. Moreover, the steady non-linear increase of the

7

cumulative numbers of publications also indicates that the research trend for the annual
publications will continue to rise in the future.
2.1.3. Presentation structure
This review offers an extensive examination of the impact of climate change on water quality
and treatment, biofiltration as a treatment technology, and the resilient and adaptive nature of
biofiltration to climate change. It also highlights the important factors that affect biofiltration
systems. The review procedures and bibliometric analyses are summarized in sections 2.1.1 and
2.1.2. Section 2.2 discusses the impacts of climate change on water quality and WTPs. Section 2.4
explores several water treatment technologies and their limitations in treating emerging
contaminants. Section 2.5 delves into biofiltration as a treatment technology, explains the removal
mechanism, and highlights the important factors that influence the removal efficiency of
biofiltration in water treatment. Section 6 outlines resilience and adaptation of bio- filtration to
climate change.
2.2. Climate change and water
2.2.1. Impacts of climate change on water quality
The consequences of climate change on water quality are threatening. The major factors that
influence water quality are increased temperature, wildfires, and extreme hydrological events such
as floods and droughts [4], [5], [15]. It is no coincidence that climate change and the hydrological
cycle are connected [16]. Usually, human activities cause water to be polluted, but climate change,
in the form of increased temperature and changes in the precipitation cycle, results in surface water
quality degradation. Climate change, primarily through warming and extreme events, contributes
to alterations in water quality parameters such as basic physico-chemical properties, DOM,
nutrients, and biological parameters [15], [17].
8

An increase in water temperature can affect the solubility and reactions of substances in water,
leading to higher concentrations of dissolved substances but lower concentrations of dissolved
gases, notably oxygen [15]. Rivers in Europe, specifically the Rhine and Meuse, experienced an
average temperature increase of around 2◦C following the severe drought of 2003, along with pH
changes and a decrease in oxygen solubility [18]. It was observed that increased DOC and
biodegradable organic matter (BOM) assimilation by microorganisms contributed to the decrease
in dissolved oxygen (DO) [19]. Additionally, floods and droughts can result in the dilution or
concentration of dissolved substances [15], [16], [20], impacting parameters such as DO and pH,
and affecting nutrient loads through runoff.
Water bodies are influenced by weather seasonality, impacting nutrient patterns, and a warmer
climate is anticipated to indirectly

increase nutrient loads in surface and groundwater,

counteracting policies aimed at reducing external nutrient loading [18], [21]. Soil warming may
result in elevated nutrient releases, runoff, and erosion, affecting pollutant transport, particularly
after drought periods. Mechanisms such as increased mineralization and runoff following droughts
and heavy rainfall mobilize nutrients from the soil of farmlands to surface water, thus leading to
speedy bacterial growth [15], [16], [22]. For instance, in Lake Taihu, China’s third-largest
freshwater lake, higher temperatures and lower wind speeds encouraged the growth of algae
blooms after nutrients were washed into the lake during tropical cyclones [23]. Cyanobacteria,
which can produce harmful toxins, benefit from warmer water temperatures and nutrient influxes,
leading to more frequent and intense blooms [15], [17]. Another example is the case of hypoxia in
Pamlico Sound, North Carolina, USA. After several hurricanes, many nutrients, freshwater, and
organic carbon were delivered into the United States’ second-largest estuarine system [24]. The
effects of weather on nutrient loading and circulation of water have also been linked to the severity

9

of summer hypoxia [16]. Higher water temperatures support the survival and spread of certain
waterborne diseases and pathogens [15].
The impact of wildfire on water quality was evaluated in the sub-regions of the Boreal ecozone
in Canada [25]. There was an increase in total phosphorus, inorganic nitrogen, and algae biomass
in the Boreal Plain lakes with burned watersheds. An increase in DOC and water colour was also
observed on Boreal Shields. The effects were associated with climate change, lake characteristics,
and lake drainage ratio. After a wildfire, an increase in erosion often leads to increased turbidity
and TSS in surface water, as well as the concentration of DOM [26]. This creates concern as DOM
can react with disinfectants during water treatment and form disinfection by-products (DBPs) [27].
An increase in organic nitrogen in stream water after wildfire may enhance the formation of
nitrogenous disinfection by-products (N-DBP) and di-halogenated acetic acid.
Climate models have predicted reduced precipitation and increased temperature for the
Mediterranean region by the end of the 21st century[28], [29], [30]. An increase in runoff and
precipitation intensity results in an increase in turbidity, thereby elevating the level of organic
matter and pollutants in water sources. In an evaluation study of the impact of climate change on
drinking WTP operations, the future (2050) water quality of the Ohio River was predicted by
modifying its 1998 baseline data [31]. The DOC, total hardness, and alkalinity of the river were
estimated to increase or decrease by + 0.036, -0.22, and + 0.03 mg/L, respectively, per year.
However, ammonia and temperature were estimated to increase by 25 % and 2℃, respectively.
Ultraviolet Absorbance (UVA), pH, turbidity, and calcium were estimated to remain the same by
2050. By the end of the century, climate change is projected to lead to higher concentrations of
water quality indicators in raw water in Norway, particularly due to an increase in rainfall [32].
This is expected to cause a deterioration in the quality of drinking water, with the most significant

10

effects anticipated in the Western and Northern parts of the country. In a situation where climate
change continues to alter weather patterns, degradation in water quality will persist. Table 2.1
shows changes in water quality of different sources over the years.

11

Table 2.1: Trends of water quality parameters over the years
Water Source

Parameters

Start Year Average

End Year Average

References

Xiangcheng WTP

Temperature (℃)

16.7

18.1

[33]

2012 – 2015

Turbidity (NTU)

25.3

104.5

NH4 - N (mg/L)

0.22

0.769

CODMn (mg/L)

2.6

2.6

Barekese Catchment

Temperature (℃)

26

32

2000 – 2019

Rainfall (mm)

76

60

pH

6.4

9.4

Turbidity (NTU)

20

50

Jinpen Reservoir

TP (mg/L)

0.02

0.09

Nov 2013 – Oct 2014

TN (mg/L)

1.3

1.5

TOC (mg/L)

2.6

2.7

Fe3+ (mg/L)

0.18

0.3

Mn7+ (mg/L)

0.07

0.08

NH4+ - N (mg/L)

0.18

0.46

Total Ammonia (mg/L)

n/a

0.05

+

12

[34]

[35]

[36]

Cerro Grande

TP (mg/L)

n/a

0.38

Northern New Mexico,
USA

Total Cyanide (µg/L)

n/a

<2.8

Total Calcium (mg/L)

n/a

245

Silver Creek

Total Ammonia (mg/L)

n/a

<0.005

British Columbia,
Canada

TP (mg/L)

n/a

0.047

Total Cyanide (µg/L)

n/a

0.8

Total Calcium (mg/L)

n/a

25.8

Parameters

Unburned watershed

Burned watershed

Grand Rapids

Fort McMurray

Water Source

Athabasca River

Total Lead (mg/ha)

8.1

10.3

Grand Rapids & Fort
McMurray, Canada

TN (g/ha.d)

3.8

3.3

TP (g/ha.d)

0.54

0.45

June 2016 – August 2016

13

[36]

[37]

2.2.2. Impacts of climate change on water treatment
Climate change influences the design and operation of WTPs just as it affects raw water
quality. The deterioration in raw water quality can lead to the inefficiency of a WTP and cause the
treatment plant to fail to satisfy water quality standards [31]. For this reason, modification may be
necessary in WTPs [38]. For example, Li et al. [31] used Monte Carlo simulations to show that the
Richard Miller plant located in Ohio, USA would be able to treat water using the existing treatment
processes by 2050. Although the total organic carbon (TOC) would be increasingly high, it could
be managed by adjusting some operational parameters. The research also showed that the expected
average operational cost would rapidly increase due to the strict performance in terms of TOC
[31], [39]. In addition, increased levels of indicator bacteria, turbidity, and colour in raw water
caused by climate change could complicate water treatment processes [32]. The impact assessment
applied to Grafham WTP indicates that future scenarios could lead to increased nitrate levels and
algal blooms, with potential consequences for WTP operations, including increased energy costs
for aeration to prevent stratification and the need for additional treatment processes to adhere to
drinking water standards [17].
Warmer temperatures can also cause more drastic weather, leading to hurricanes, heavy winds,
cyclones, hail, floods, and thunderstorms, which could potentially cause damage to water treatment
infrastructure. The United Nations International Panel on Climate Change predicted a sea level
increase of 18 – 58 cm in the 21st century, which does not include future dynamic change in
ice flow [40]. This increase in sea level will affect WTPs located close to water bodies
influenced by tides. Additionally, wildfires reduce vegetation, allowing more
precipitation to reach the land surface [41]. Exposure of the land surface to rainfall
increases erosion and runoff. Wildfire-impacted waters are rich in pollutants such as

14

heavy metals, nutrients and natural organic matter (NOM), and the long-term release into
the water column drastically reduces the quality of water and affects the performance of
WTPs [26], [27], [42]. Furthermore, the rise in particulate level, NOM and DOC
concentration affects the coagulation process in conventional WTPs because the
coagulant demand is increased and its effectiveness is compromised [6], [26], [27]. Thus,
this increases the costs of infrastructure and operation, disrupts services and causes
outages [26], [43]. Table 2.2 highlights past and predicted climate events and their impacts
on water quality and WTPs.

15

Table 2.2: Summary of the impact of climate change on water quality and WTPs
Climate events (past and future predictions)
Impacts on Water Quality and WTPs

References

Series of hurricanes in Pamlico Sound, North Carolina,

Nutritional loading and circulation in water led to extreme cases of summer

[24]

USA.

hypoxia.

Wildfire in Boreal ecozone in Canada.

Increase in TP, N, and algae biomass in Boreal Plain lakes and increase in DOC [25]
and water colour in Boreal shields.

Increased sea level and tides in the 21st century.

Flood and damaged water treatment facilities located close to water bodies.

[40]

Hurricanes, cyclones, hailstorms, and thunderstorms.

Heavy winds and floods destroy water treatment infrastructure

[44]

Lowered precipitation and increased temperature in the Increase in turbidity, organic matter, and pollutants in water sources

[45]

Mediterranean region at the end of the 21st century.
Future water quality of Ohio River, USA in 2050.

Increase in water hardness, DOC, alkalinity, ammonia, and temperature.

[31]

Warmer temperatures and lower wind speeds in Lake Growth of blooms on the lake after nutrients from vegetation were washed into [23]
Taihu, China.

the Lake.

Warmer temperatures.

Boost biodegradation and enhance pollutant removal efficiency

[46], [47]

High-intensity rainfall or increased precipitation.

More volumes of raw water increase WTP energy cost

[46]

Wildfire

Increases NOM, nutrients, and metals, thus reducing water quality and

[26]

increasing operational costs, disrupting services, and causing outages.

[43]

Increases erosion and runoff by reducing vegetation

[41]

Wildfire

16

2.3. Drinking water treatment technologies resilient to climate change
Drinking water treatment methods adopted for treating water depend on the quality of the raw
water. This section highlights and compares some of these methods.
2.3.1. Conventional filtration
Filtration using media such as sand is a traditional treatment method that removes fine particles
and dissolved organic matter from raw water as it passes through a porous bed of filter medium
[48], [49]. Conventional filtration includes screen filters, slow and rapid sand filtration, diatom
filters, and charcoal filters [50]. Conventional filtration alone has been demonstrated to be
insufficient in treating water to the required effluent quality. To remove diclofenac from potable
water, traditional sand filtration was compared to GAC filtration [50], [51]. While sand filtration
was insufficient in removing diclofenac, GAC filtration achieved over 99.7 % of removal. GAC
filtration works efficiently for micropollutant removal, but its application is expensive, which is
concerning in underdeveloped and developing countries [49], [52]. Sabogal-Paz et al. [53]
conducted another study examining the impact of flow configuration in a slow sand filter,
specifically comparing intermittent and continuous flows in a household system. It was found that
the flow configuration of a slow sand filter alone is insufficient for effective treatment, as it fails
to adequately remove organic foulants. The limitations of conventional filtration necessitate the
need to consider alternative treatment methods.
2.3.2. Adsorption on activated carbon
Adsorption, a cost-effective and efficient advanced water treatment technology, involves the
separation of pollutants (adsorbates) in raw water (liquid phase) and their adsorption onto the
adsorbents [54], [55]. The removal capacity of adsorbents is dependent on material and bulk
densities, porosity, surface area, pore-size distribution, and operational parameters such as

17

temperature, pH, and contact time [54], [55], [56]. Activated carbon is the most used material
because of its high specific surface and porosity, which makes it efficient in removing
contaminants [57]. Nano adsorbents and engineered nanomaterials have been developed recently
to enhance the removal of emerging pollutants [55]. Research have shown that activated carbon
adsorption is efficient in treating emerging pollutants which have been in the increase in the phase
of a changing climate [58], [59]. In a study to improve the removal of estrogenic and
pharmaceutical compounds from river water polluted by sewage effluent using full-scale GAC,
significant reduction in pharmaceutical compounds – mebeverine (84 – 99 %) as well as
carbamazepine and propranolol (17 – 23 %) [58] was achieved. In the determination of the
adsorption capacity of ciprofloxacin on three types of carbon-based materials: activated carbon,
carbon nanotubes, and carbon xerogel, Carabineiro et al. [59] demonstrated that activated carbon
selectively removes emerging pollutants present in raw water. However, there is a gap in the
current knowledge about the influence of other parameters on the performance of adsorption
systems and a lack of understanding of scale-up parameters. Additionally, most of the research
studies focused on lab-scale tests, which do not provide insight into the feasibility of full-scale
processes.
2.3.3. Coagulation and flocculation
Coagulation and flocculation, which are traditional water treatment methods, when combined,
have proven to be highly effective in removing suspended particles [54]. These processes
precipitate metal hydroxides, remove suspended particles and inorganic contaminants present in
raw water [49], [52], [54]. The primary objective is to neutralize and destabilize small particles,
facilitating their collision to form agglomerates [60]. The small particles in water are mostly
negatively charged, repelling each other and preventing agglomerate formation. Neutralization,

18

which is crucial in the removal of these stable particles, is achieved using chemical agents known
as coagulants. The most used coagulants are aluminum sulfate and iron salts, such as ferric sulfate
or ferric chloride [54]. These coagulants carry a positive charge that attracts the negatively charged
particles, leading to the formation of new neutral particles. Snyder and Kim’s research removed
30 % of dichlorodiphenyltrichloroethane and 70 % of benzo(a) pyrene in bench-scale coagulation,
flocculation and sedimentation experiments [61]. Although coagulation and flocculation are
efficient in removing organic compounds, they are less effective in removing polar and semi-polar
pharmaceuticals and pesticides [52], as well as organic micropollutants not attached to
agglomerated particles [49].
2.3.4. Membrane processes
Membrane processes are effective in the removal of solids, organic, and inorganic particles
from raw water using materials that support the separation of contaminants (retentates) from water
(permeate) [55], [62]. The materials' characteristics, such as hydrophobicity and pore size, used in
the production of membranes determine the type of contaminants that can be retained [55]. Several
membranes such as microfiltration, ultrafiltration, nanofiltration (NF and reverse osmosis (RO)
can be used for potable water treatment [49], [54], [55], [62]. The type of membrane used for
treatment is dependent on the raw water quality, targeted contaminant to be removed and the
drinking water treatment requirements [55]. Several researchers have used membrane processes to
achieve denitrification of nitrate, ammonia removal, and TOC reduction [63], [64]. In an
investigation of nitrate removal from polluted groundwater using two-stage anoxic/oxic biofilm
membrane bioreactor, Ravnjak et al. [63] achieved 99 % nitrate conversion at a residence time of
the liquid phase equal to 2.5 h. In another study, a laboratory-scale membrane bioreactor using a
submerged polyethylene hollow-fibre membrane module was used to treat a raw water source

19

slightly polluted by domestic sewage [64]. Results showed that there was a significant reduction
in the concentrations of TOC, NH3-N, turbidity, and total coliforms. Although NF and RO are
efficient in treating pesticides, pharmaceuticals and personal care products (PPCPs), and toxic
metals [55], their major shortcoming is membrane fouling [49], [50], [54]. Membrane fouling
reduces the efficiency of membrane and shortens their life span [49]. Membrane fouling requires
chemical cleaning which increases operational cost [49], [54]. Additionally, pollutants retained on
membranes are transferred, not degraded [55], [65]. Thus, requiring secondary treatment and
disposal.
2.3.5. Advanced oxidative processes and disinfection
Advanced oxidative processes (AOPs) are now commonly applied for water treatment. AOPs
involve the generation of hydroxyl radicals for continuous oxidation of organic and inorganic
pollutants available in raw water [50], [54], [66]. Hydroxyl radicals are potent, non-selective
chemical oxidants that oxidize most water-based compounds through two mechanisms [54]. The
radical first breaks the double bond between two carbon atoms and adds to them. In the second
mechanism, the radical reduces the amount of hydrogen bonded to the carbon. Key AOP methods
include ozone, hydrogen peroxide, UV light, combining Fenton’s reagent with hydrogen peroxide,
combining ozonation and hydrogen peroxide, and using UV irradiation with hydrogen peroxide
[54], [55]. AOPs are versatile and can address concerns such as color removal, oxidation of
synthetic organic chemicals, and taste and odor control in water treatment [50], [67]. Also, they
can degrade emerging contaminants such as pharmaceuticals [54]. However, the complexity of the
chemical reactions involved in AOPs makes designing an optimal treatment system challenging.
They are cost and energy intensive and not sustainable for low- and middle-income countries [50],
[52], [55]. Additionally, AOPs produce toxic secondary effluents and DBPs from the degradation

20

of contaminants when chemical oxidants are used [49]. Further- more, small concentrations of
chemical oxidants can be found in the treated effluents [55].
2.4. Biofiltration as a low-cost techno-ecological nature-based solution for water treatment
2.4.1. Biofiltration as a water treatment technology
Biofiltration is an established, chemical-free, low energy, and efficient biotechnology used in
removing pollutants present in water [68]. It is the process by which a filter, with attached
microbial biomass on the surface, is used to remove suspended and fine particles and degrade
dissolved organic compounds through physicochemical and biological processes [11], [68], [69].
Although it was initially a biological air pollution control technology with a reported removal
efficiency of 95–99 % of volatile organic compounds, it is now applied for the purification of
surface waters for potable use [68], [70]. The use of biofilters for potable water treatment started
after the discovery of the regrowth of microbial mass on the inner surface of pipe- lines used in
distributing water [68], [70]. This microbial regrowth results from pollutants such as BOM,
ammonium, sulphur, and DBPs in water.
The primary purpose of biofiltration in water treatment is to produce biologically stable and
potable water that is clear of microbial growth during distribution. It also controls the formation
of DBPs. Research has shown that biofiltration is efficient in reducing the content of BOM and
electron donors that cause biological instability in pipelines [70]. The capacity of biofiltration to
remove organic compounds that support the growth of microbial mass in distribution systems
makes it a salient technology in water treatment. This technology reduces the need and use of
chemicals in water treatment, thereby making the application of biofiltration a green and
sustainable engineering technology [11], [71]. It is also a cost-effective, and eco-friendly

21

conventional contaminant removal method, compared to other methods such as chemical
coagulation, membrane separation, ion exchange, ozonation, and many more [71].
2.4.2. Pollutant removal mechanism in biofiltration during water treatment
In biofiltration, the primary treatment mechanisms are filtration (physical separation),
adsorption, and biodegradation [68], [70], [72]. A nutrient-rich rough layer gradually formed on
the filter media by suspended particles attracts microorganisms, which colonize it, forming
biofilms on the filter media surface over time [68]. When raw water is made to pass through a
biofilter, organic matter which serves as carbon to the microorganisms, is absorbed into the
biofilm. Using the absorbed organic matter as a carbon source, the microbes grow well to form
colonies that end up degrading the other contaminants [71]. The elimination of inorganic
contaminants that are not biodegradable primarily depends on their adsorption onto the filter media
or biofilms [14]. GAC has stronger adsorptive capacity compared to non-adsorptive media such as
anthracite and sand. Some contaminants can be adsorbed onto the GAC; however, the adsorption
capacity is limited and can become exhausted, leading to the breakthrough of adsorbable
compounds [72]. Less biodegradable substances may also desorb from the GAC. After a period of
acclimation, which can vary in length depending on the contaminant and the source of the filter
media, biodegradation and biotransformation become the main removal mechanisms [72], [73],
[74]. During acclimation, microorganisms grow on the filter media, forming a biofilm that
degrades contaminants. This process occurs on the media surface and sometimes within
macropores [72]. Understanding the natural behaviour of microorganisms and their biochemical
reactions is vital in designing and operating biofiltration systems for water treatment. Biofiltration
has also demonstrated its capability of removing micropollutants through microbial degradation

22

rather than physical straining. Figure 2.3 provides a visual overview of the operational principles
of biofiltration.
Influent

Biofilm/Microbes

Media
Pollutant

Effluent
Figure 2.3: Mechanism of biofiltration systems.
2.4.2.1. Attachment of microorganisms
For biofilm development on the filter media, microorganisms are transported to the surface of
the filter media through either natural attachment or artificial immobilization [71]. To facilitate the
natural attachment process, nutrients are introduced to the filter media to support the accumulation
of a microbial community [68]. For artificial immobilization, the movement of the microbes is
controlled through diffusion, convection, gravity sedimentation, or the active mobility of the
microorganisms [14]. Bacteria by-products, EPS, which also support molecular distribution and
23

conversion of DOM to smaller molecules, are used by microorganisms to adhere to the filter media
[14], [75]. Microbes are immobilized in the biofilter bed to achieve the best production rate of
microbial products through improved metabolism and increased loading cells. The attachment of
microorganisms to the filter media is the most critical parameter that governs the biofiltration
process. It has been suggested that a microorganism’s attachment and colonization depend on the
bulk fluid, cell structure properties, and substratum or filter media surface properties [70], [76].
The substratum’s properties, such as texture, roughness, and hydrophobicity, can enhance
attachment by preparing specific cell surfaces to be drawn to the media. Furthermore, bulk fluid
conditions, including nutrient levels, flow velocity, pH, and the presence of inhibitors, can
encourage cell attachment and microbial colonization of the media due to the availability of food
and protection from the surrounding environment [76].
2.4.2.2. Growth of microorganisms
Once attached to the media, the microorganisms slowly develop on the surface of the biofilter
media to form a slime layer known as a biofilm. The microbes secret EPS which contains proteins
and polysaccharides as the major components as well as other components including uronic acids,
lipids, humic acids and nucleic acids [77]. Polysaccharides instigate covalent bonds during
colonization and form an active layer on the biofilter surface [71]. This active layer is responsible
for capturing the contaminants [71]. The development could take up to several months, depending
on the organic concentration of the influent. As explained by Fick’s first law, the substrate moves
to the outer surface of the biofilm from the bulk liquid, where it diffuses into the biofilm for its
metabolism. Factors such as mass transfer of substrate to the biofilm, substrate diffusion into the
biofilm, and active utilization within the biofilm affect the substrate utilization rate. Fick’s second
law then describes the mass transfer of substrate to the biofilm, while Monod’s expression

24

describes the substrate utilization. Biomass growth could also be affected in an unsteady
condition [70].
2.4.2.3. Decay and detachment of microorganisms
The success of a biofilter depends on the preservation and detachment of the biomass attached
to the biofilter media. Biomass is detached using grazing, erosion, filter backwashing, abrasion, or
sloughing. Fluid shear causes biomass erosion, while abrasion is the collision of an outer particle
to scrape off the bio cell from the surface. Sloughing detaches biomass in large patches, and some
sections of the biomass on the outer shell get lost to protozoa grazing. Most studies have focused
on biomass detachment due to shear [70], [78].
2.4.3. Factors influencing biofiltration
Several factors (biological, chemical, and physical) may influence the removal efficiency of
biofiltration in water treatment applications. These factors include type and concentration of
microorganisms, type of filter media, nutrients supply, operating temperature, empty bed contact
time (EBCT), pH, hydraulic loading rate (HLR), frequency of back- washing and filter media
replacement [68], [71], [79].
2.4.3.1. Type and concentration of microorganisms
The major component in the biofiltration process are the microorganisms. They are crucial
contributors to the removal and degradation of pollutants. Additionally, they play a significant role
in the co-metabolism of diverse organic micropollutants [68], [80]. The microbial community
formed on the filter media comprises nitrifying bacteria, methane-oxidizing bacteria, ironoxidizing bacteria, and heterotrophic bacteria [68]. Although these catalytic microorganisms
include viruses, fungi, protozoa, and bacteria [71], heterotrophic microbes – fungi and bacteriaare the most common and predominant. They attach and colonize the surface of the biofilter bed

25

and degrade the contaminants [71]. The most resistant microbial population is selected after
acclimatization. The biodegradability level of a pollutant and the nature of the filtering materials
affect a biofilter bed inoculation. Thus, this demonstrates the importance of selecting and using
the appropriate type and concentration of biological organisms.
The microbial communities in biofilters, primarily shaped by water quality, are complex and
contribute to addressing safety challenges in drinking water. However, research may underestimate
the active role of these microbiomes in biofilters. Bai et al. [68] indicated that biofilters are
composed of diverse microbiomes, with conventional biofilters significantly contributing to water
safety. The microbial ecology among rapid sand filtration (RSF), granular activated carbon
filtration (GACF), and SSF vary with each process featuring distinct microbial communities that
influence the removal of different contaminants [68].
2.4.3.2. Type of biofilter media
The type of biofilter media used significantly influences biofiltration processes. Biofilter media
type and pollutant biodegradability affect biofilter bed inoculation. The filter media provides a
surface for biofilm growth after the microorganisms have been transferred [81]. Common media
used for the construction of the biofilter bed are sand, GAC, and anthracite. These materials are
considered due to their pore size distribution, cost effectiveness, versatility, and permeability as
well as large and irregular surface area [82], [83], [84], [85], [86]. The mentioned features enable
a biofilter to support the homogenous distribution of influent, enhance the metabolism and growth
of microbes responsible for biofilm formation, and reduce the compression of biofilters. It was
stated that combining organic materials and inert bulking materials increases filter media's useful
life. Table 2.3 below shows some characteristics of materials used in constructing biofilter beds.

26

Table 2.3: Characteristics of biofilter media
Biofilter media
GAC and Anthracite

Sand

Anthracite

Characteristics

DOC Removal (%)

References

•

Random pore structure and pore size distribution

> 94%

[82], [83], [87]

•

Strong adsorptive capacity

•

Higher molecular weight

•

Cost-effective and versatile.

•

Provides a constant barrier against water pollutants.

•

A large mass of carbon provides a large surface area.

•

Affordable and easy to operate.

41 – 98%

[84], [85]

•

Good permeability

•

Reliable and durable.

•

High pollutant removal efficiency

•

Good water holding capacity.

•

Large and irregular surface

≤20%

[86], [87], [88]

•

Protects microbes from shear loss.

•

Belongs to the hydrophobic group (C-C and C-H)

•

Good adsorbent for organic matter removal

•

Contains low impurities

27

Sand, GAC, and anthracite are common media used in standard biofilters for water purification.
The adsorptive capacity of GAC to remove specific pollutants makes it the most commonly used
filter media in potable water treatment processes [11]. GAC is referred to as biological activated
carbon (BAC) when its intended use is as a biofilter growth medium for microorganisms. BAC
filters, with their rough GAC grain surfaces and the ability to accumulate and sorb substrate within
the particles, offer ideal conditions for biofilm growth and are more effective at removing trace
organics than other media filters [76]. Additionally, GAC grains can mitigate the impact of
inhibitors in the influent through sorption and desorption processes. This raises the question of
whether BAC enriches certain specialist microorganisms with specific cell characteristics or
whether it provides a suitable environment for generalist microorganisms from the feed water to
scavenge low substrate concentrations more effectively.
Compared to sand and anthracite, the porosity, surface area, and roughness of GAC support
the growth of microbial communities 4 – 8 times more in biomass per gram [89]. GAC can be
regenerated due to its ability to adsorb and retain biodegradable parts of the contaminants. It also
has a lesser specific surface area and an irregular surface, which makes biomass attachment easy.
Kumar et al. [90] reported that the integration of activated carbon into filter bed materials improved
pollutant biodegradation. When comparing the performance of GAC and anthracite as primary
dual media filters, Thiel et al. [91] reported that DOC removal was higher with GAC (15 – 20%)
compared to anthracite (2 – 7%) at EBCT of 16 min. At an EBCT of 8 mins, DOC removal ranged
between 11 – 14% using GAC and 1 – 3% using anthracite. Emelko et al. [92] observed that TOC
removal at temperatures in the range 21 - 24℃ was higher using GAC (23%) compared to
anthracite (14%). Although several media types can be used in biofiltration, GAC has proven to
be a robust media that supports microbial growth and attachment [11].

28

2.4.3.3. Operating temperature
The removal of BOM within biofilters is related to temperature. The number of active
microorganisms and the degradation of contaminants are dependent on the operating temperature
of a biofilter [71]. Temperature changes microbial structure, which in turn affects the substrate
metabolism rate [71]. Microbial activities are low at lower temperatures (<10 ℃), thus reducing
pollutant removal efficiencies [11]. The removal rate of contaminants is greater at a higher
temperature [79]. According to Liu et al. [79], the removal rate of BOM is slightly higher at 20℃
than 5℃ in the absence of chlorine while using a GAC filter. This is different in the presence of
chlorine; the removal rate is greater at a higher temperature. Chaudhary et al. [70] showed that
biomass adapted at 10℃ but increased with increasing temperature up to 30℃. A temperature
increase from < 10℃ to 10℃ - 20℃ to > 20℃ was recorded to have improved the removal
efficiency of TOC from 10 % to 12–17 % respectively in a review of the performance of BOM
and rapid-rate biofilter [69]. These studies support the notion that an increase in temperature
enhances pollutant removal efficiency.
Contrarily, Liu et al. [79] observed that cold temperature (5℃) could favour biofiltration
provided that the biofilter was backwashed in the absence of chlorine. Additionally, Emelko et al.
[92] also reported that the removal of TOC was not significantly affected by temperature while
using anthracite and GAC. It was noted that TOC removals by the GAC filter adsorber were
slightly higher during the cold-water season compared to those observed during the warm water
season, but these differences were not statistically significant. It was also observed that BOM
removal may be affected at low temperatures. Moll et al. [93] observed significantly low removal
of NOM and the NOM that reacts with disinfectants when the biofilter was operated at 5℃.
Hoyland et al. [94] also noted a decrease in Mn removal as influent temperature decreased. When

29

the water temperature began to increase, Mn removal increased, eventually returning to greater
than 98 % Mn removal.
The biofiltration guidance manual for drinking water facilities highlighted that biofiltration is
most efficient at warmer temperatures (> 15◦C) due to the doubling of the reaction kinetics at every
10◦C increase [72], [74], [95]. Additionally, [96] observed that the biofilm formation rate increases
significantly at WTPs with temperature greater than 15°C.
2.4.3.4. Empty bed contact time
The EBCT is an important operating parameter that was introduced by Zhang and Huck in
1996. EBCT was incorporated alongside the specific surface area of a biofilter, biodegradation
rate, and substrate diffusion rate [70]. Additionally, a change in filter depth and hydraulic loading
can lead to an increase in EBCT [70]. Vines and Terry [97] observed that the protein-like
component of NOM was degraded efficiently from the lake and river water with increasing EBCT
(15 – 30 mins). The study showed that EBCT for more than 5 min aids the removal of additional
fractions of fluorescing components. A 30 – 50 % rise in TOC removal was recorded with an
increase in EBCT of 5 – 20 min. Although the highest DOC removal (73 ± 6 %) was achieved at
an EBCT of 30 min with a biological ion exchange (BIEX) filter, lower EBCTs could improve the
NOM removal rate when operated concurrently at higher temperatures [98]. The study concluded
that EBCT and temperature should be considered concurrently for an efficient removal of NOM
using biofilters. Moona et al. [99] observed that the removal efficiency of DOC, protein-like
(F280/340), and microbial humic-like (F290/420) fluorescent organic matter was high with
increasing EBCT. The results from this study showed that longer EBCT (> 30 min) improved the
operations of biofilters, thus, proving that longer EBCT improves pollutant degradation. Similarly,
Terry and Summers [69] observed that biofilters removed 12 % of the influent TOC at an average

30

EBCT of 12 mins. Ko et al. [100] observed that there was a higher DOC removal with higher
EBCT, highest being 20 mins. Although, the typical EBCT of biofilters ranges between 5 and 15
min [101], an improvement in contaminant removal is noted when the EBCT increases by 10 mins
at temperatures higher at 15°C [72], [74]. Nevertheless, an EBCT > 10 mins is advantageous for
the removal of Trace Organic Compounds when the temperature is < 15°C [72], [74].
2.4.3.5. pH
The role of nutrients in enhancing microbial activity and degrading pollutants is feasible under
optimum pH. Although the optimum pH for biological processes in most drinking water sources
ranges between 6.5 and 8.5 [72], [95], some microorganisms perform better under extremely acidic
conditions. Thus, the ideal pH of the microbial population of interest has to be considered when
designing proficient WTPs. Heterotrophic microorganisms that survive on biofilter beds operate
at a neutral pH of 7. It has also been demonstrated that the disparity in pH influenced removal
performance and microbial activity. Acidification causes pH disparity which in turn impedes a
biofilter’s performance. In the evaluation of the potential of biological Mn removal at the lower
pH conditions (6.2–6.3), four laboratory-scale biofilters were operated over a pH range of 6.3–7.3
[94]. At pH 6.3, Mn was oxidized and over 98 % of Mn was removed. In addition, over 90 % of
the simulated organic ozonation by-products was removed in all columns.
In a study to engineer the environment of a biomass structure in biofilm and biofilter on a
reverse osmosis membrane, Jeong et al. [102] observed a 2 % increase in the removal of DOC
when the pH was adjusted from 7.5 to 6.5. Similarly, Granger et al. [103] noted different responses
of nutrient-enhanced biofilters to pH adjustment. For filters with a carbon-phosphorus (C:P) ratio
of 100:15, an average 6 % increase was noted when the pH increased from 6.0 to 9.0, while filters
with a C:P ratio of 100:3 observed an increase of 14 % on average [103].

31

2.4.3.6. Hydraulic loading rate
The hydraulic loading rate (HLR) is an essential factor in water treatment efficiency. Increasing
the HLR results in the reduction of removal efficiency. A study investigating the removal
efficiencies of five trace-level emerging organic contaminants (EOCs) and the microbial response
in two identical drinking water treatment residue-based bio-filters under different HLR conditions
found that higher HLR boosted the EPS production, increased community diversity, and enhanced
denitrification [104]. This improved the removal efficiency of biodegradable EOCs like
roxithromycin (80 %) and sulfamethoxazole (76 %). In contrast, lower HLR caused carbon
limitation and selective pressures, enriching important species such as Bacillus, which play a
crucial role in EOC degradation.
A pilot-scale system study was conducted with three dual-media GAC and sand biofilter
columns running in parallel [100]. The HLRs for the filters were set at 24 m/h and EBCT of 5 mins
for filter 1, 12 m/h and EBCT of 10 min for filter 2, and 6 m/h and EBCT of 20 mins for filter 3.
The DOC removal efficiencies were 31 %, 26 %, and 19 % for filters 3, 2, and 1, respectively,
suggesting that a higher EBCT and lower HLR are preferable for DOC removal [100]. An
investigation on the effects of HLR on semi-volatile organic compounds (SVOC) removal and
microbial community structure in drinking water treatment biofilters was conducted using six
biofilters [105]. At an optimal HLR of 3.0 m/h, over 85 % of chemical oxygen demand and
assimilable organic carbon was removed. Although results showed that increased HLR reduced
the removal efficiency of SVOCs, up to 84 % of the main SVOCs were removed (71.2 % of Di-nbutyl phthalate and 84.4 % of bis(2-ethylhexyl) phthalate). At the lowest HLR, nearly 65 % of 2,6dinitrotoluene and 80 % of isophorone were removed. The study also confirmed that the

32

dominance of E. coli in low-HLR biofilters likely contributed significantly to the high SVOC
removal.
In a study that explored biological Mn removal at the lower pH conditions (6.2–6.3) using four
laboratory-scale biofilters operated be- tween pH 6.3 and 7, the stress tests showed that wellacclimated manganese oxidizing bacteria could endure variations in hydraulic loading rate (1.36
× 10-3 – 2.72 × 10-3 m/s), Mn concentration (0.1 – 0.2 mg/ L), and temperature (7 – 22℃)
commonly found in surface water treatment plants, at least for short periods (1–2 d) [94].
2.4.3.7. Backwashing
Filter backwashing is the periodic cleaning of the filter media bed with water or a combination
of air scour and water [106]. This process fluidizes the filter bed to remove deposited material that
can clog the filter pores and increase the head loss within the bed [106]. It has been shown that
biomass detachment, biomass and accumulated particle removal, media distribution, and oxidant
impact on backwash water are all dependent on adequate backwashing [11]. Kim et al. [107] and
Qi et al. [108] indicated that regular backwashing, using water or air scour, and water are the most
efficient methods for granular rapid filters to maintain the adsorption performance of GAC filters
and prevent secondary contamination of the filter effluent until regeneration is required. Liao et
al. [109] observed the impact of the backwashing process on the biomass and biofilm structure
within a pilot-scale BAC filter to improve the removal of DOC. Results showed that backwashing
was found to significantly influence the bacterial diversity and community composition of the
BAC biofilm. However, these effects were seen to be gradually restored as the filtration process
continued after the backwashing.
Similarly, Kim et al. [107] reported that filter backwashing temporarily disrupted the bacterial
diversity, but the community was swiftly re-established during the subsequent operation. In a study

33

to observe the influence of backwashing on the BAC filter process, with respect to the removal of
non-coagulable dissolved organic carbon (NC-DOC) and the composition of the microbial
community, a laboratory-scale BAC filter was operated for a duration of up to 5 months, with
backwashing conducted every 5 d [110]. It was found that when the BAC filter is backwashed at
the optimal frequency, certain advantages, such as the biofilm developing a higher abundance of
BOM degrading bacteria and a lower population of opportunistic pathogenic and filter clogging
bacteria, as well as additional benefits for the coagulation process, can be realized. In the
examination of the impacts of air scour and sub-fluidized backwash on oxalate and TOC removal
at warm and cold temperatures, it was observed that the backwash conditions had little to no effect
on BOM removal, but oxalate removal was high when air scouring was used under cold
temperatures [92].
Ikhlef and Basu [111] investigated the effects of varying backwash methods on DOC reduction,
backwash water usage, and effluent water clarity on a lab-scale biofiltration setup. Three dual
media biofilters were run concurrently, each with 520 mm of GAC atop 180 mm of sand and 15
mm of synthetic drainer as support. Results indicated that DOC removal improved from 13 – 21
% when collapse pulsing was introduced to the backwash cycle under nutrient-scarce conditions
compared to simple water backwashing. Reducing the bed expansion from 30 % to 20 % did not
compromise DOC removal (35 %), however, backwash water consumption reduced by about 20
% [111]. For efficient performance, Liu et al. [79] recommended that biofiltration be performed at
lower temperatures without chlorine when dealing with easily biodegradable compounds. Youqun [112] also highlighted that backwashing with chlorinated water had negative impact on
biofilms.

34

2.4.4. Dominant microbes in biofiltration
The structure, function and dynamics of the microbial communities significantly contribute to
biofiltration process, thus necessitating the need to customize these communities to improve
treatment efficacy [113]. Putative keystone taxa that are necessary for maintaining the functional
stability of systems were identified with the help of microbial co-occurrence networks using
random matrix theory (RMT) [113], [114]. Keystone taxa are interconnected species that help
shape microbial community structure and function, regardless of their abundance over time and
space [113], [115]. Their removal can lead to significant changes in microbiome function and
ecosystem processes. An investigation was conducted to study the interaction of microbial
communities in BAC filters. The co-occurrence network indicated that microbes in the BAC filter
are likely more collaborative than competitive. Phylum Proteobacteria, and Cyanobacteria were
identified with relative abundance of 0.27 %. In addition, genus Hyphomicrobium, Tabrizicola,
Phreatobacter, Candidatus Obscuribacter, Pseudorhodoplanes, and Rhodospirillales with
relative abundance of 0.08–0.38 %, 0.27 %, 0.15 %, 0.21 %, 0.05 %, and 0.01 %, respectively,
were identified. However, the bacterial community identified in the BAC using metaproteomic
and metagenomic analyses are Sphingomonas, Gemmata, Belnapia, Hyphomicrobium, and
Crenalkalicoccus. Despite the relatively low abundance of bacterium Hyphomicrobium, it
contributed 0.3–1 % more to the most prevalent functions and produced 5–21 more proteins/g
GAC compared to the dominant bacterium Sphingobium.
In the investigation to identify narrow host range phages infecting essential bacteria in
groundwater-fed RSFs, 24 groundwater-fed RSFs were analysed in Denmark [116]. The analysis
identified a core phageome commonly found in these systems. This core phageome is linked to
dominant microbes essential for water purification processes. In addition, Palomo et al. [116]

35

found that Siphoviridae, Myoviridae, and Podoviridae were dominant species of the taxonomically
classified viral sequences, and they accounted for 57 %, 28 %, and 14 %, respectively. The hosts,
predicted using the multiple in silico prediction methods, encompassed 15 bacterial phyla, with
the Hyphomicrobiaceae family accounting for 21 % of virus-host pairs and the Nitrospiraceae
(most predicted) family for 15 %. With 77 % of the detected phages being lytic and numerous
specific phage-bacterial interactions, it’s suggested that phages help control bacterial populations
by predation, thus preventing the dominance of any single species and maintaining functional
redundancy. In summary, bacteriophages are likely crucial in water treatment within biofilters
through their interactions with key bacterial species.
2.5. Resilience and adaptation of biofiltration to climate change
As a nature-based water treatment technology, biofiltration has proven to be remarkably
resilient and adaptable to climate change. The resilience of a system refers to its capacity to
withstand and recover from disruptions, while adaptation describes a system’s ability to respond
and adjust to long-term changes [117]. Biofilters house diverse microbial communities that are
essential to the treatment process. These communities are flexible and can adjust to changing
environmental conditions. As climate change causes variations in temperature, precipitation
patterns, and contaminant levels, the microbial communities present in biofilters can adapt and
retain their pollutant removal efficiency. This system has consistently shown strong treatment
performance under extreme weather conditions such as rainfall, wildfire, drought and many more.
These extreme weather events affect the HLRs, but biofilters exhibit stability and maintain their
effectiveness in pollutant removal. The ability of microbial communities to self-regulate and
recover from climatic disruptions explains the resilience.

36

Climate change increases the difficulty in the treatment of water and its source quality. Extreme
events such as wildfires pose more concerning threats because significant drivers of water
treatment technique design and optimization, such as turbidity and DOM are affected [6], [27].
Changes in DOM concentrations challenge the removal capacity of standard treatment
technologies because they increase the formation of DBPs. In an experimental research on removal
of organic pollutants of drinking water by bio- filtration, results showed that biofiltration could
remove trace organic pollutants effectively [112]. The resilience of biofiltration to wildfire ashassociated organic carbon threat to potable water treatment was investigated and results showed
that biofiltration offers resilience by softening the altered DOC concentration and improving the
consistency of potable water in distribution systems [6]. In a 2-d and 7-d wildfire ash disturbance
period, the removal rate of DOC increased in biofilters removing wildfire ash-amended water
compared to the control biofilters. Water extractable organic matter of the readily biodegradable
low molecular weight neutrals present in the wildfire ash amended biofilter influent makes DOC
removal easier versus the control biofilters treating the source water baseline. It was also
discovered that the control biofilter removed more humic substances, a driver of UV254
absorbance [118] compared to ash-amended water. The authors concluded that if the balance
between the DOM recalcitrant and readily removed fractions changes, the resilience of biofiltration
might be compromised. However, it can be attenuated by using coagulation to treat DOM fractions
with lower biodegradability before using the biofiltration process.
The response to drinking water treatment after a Colorado, USA wildfire was studied [27]. It
was noted that wildfire influences DOM character and conventional processes could still be used
to treat postfire source water. To do this, a higher dose of coagulant was needed for water
treatment. Samples collected post-rainstorm were unresponsive to conventional water treatment;

37

lesser DOC was removed and DBP formation was high. NOM removal in potable water treatment
was investigated using two-stage biofiltration – GAC and sand-anthracite (SA) [119]. High NOM
and turbidity removal efficiencies were demonstrated. Sand anthracite biofilters were used at the
treatment’s first stage and effectively removed > 74 % turbidity. GAC contactors used in the
second stage removed NOM: > 24 % DOC, > 547 % UV254, and > 44 % SUVA254. GAC
contactors removal efficiency for UV254 can be improved by pre-oxidation.
In research aimed at removing NOM and emerging contaminants such as pharmaceuticals and
personal care products using two-stage biofiltration for drinking water treatment, a combination
of sand/ anthracite (SA) biofilters and biologically active GAC post-filters were employed [119],
[120]. Results indicated that biofiltration process can effectively remove turbidity, NOM, and an
average of 53.4 % of PPCPs, with SA filters removing turbidity and GAC filters contributing
significantly by removing 48.1 % of the PPCPs and NOM. It was observed that smaller molecules
with less complex structures were more effectively removed. Additionally, a regression model was
created to forecast the removability of specific PPCPs. The study also highlighted that biofiltration
reduced health risks by an average of 79 %, suggesting that the levels of PPCPs in treated water
would not pose significant health risks.
Ongoing research in biofiltration processes has led to improved designs, materials, and
operational strategies. These innovative techniques provide improved adaptability to climate
change, optimize pollutant removal efficiency, boost system durability, and address some
challenges caused by climate change [71]. While biofiltration exhibits resilience and adaptability,
it is necessary to observe that the effectiveness of biofiltration systems can be influenced by local
factors, maintenance practices, and the specific pollutants being treated. To ensure continued
resilience and adaptability in climate change, the following adaptation strategies have been advised

38

[6], [117]: 1) altering media maintenance and replacement based on humidity distribution and
decay rates, and 2) regular monitoring, proper maintenance, and periodic assessment of system
performance.
2.6. Challenges of biofiltration
In biofiltration, filter media such as sand and GAC naturally support the growth of diverse
microbial communities, thus promoting biofilm formation [50], [121]. As a result, these filters not
only act as adsorptive media but also support biomass substrate of microbes that utilize the readily
available carbon in the system [49], [122]. Previous lab- and pilot-scale research indicate that sand
filter-associated bacterial communities can potentially degrade contaminants and micropollutants
[49]. Although Wang et al. [123] considers biological filtration to be the most efficient in
producing biologically stable water, there are concerns regarding the appropriate design and
implementation of this treatment process, especially regarding the size and type of filter media to
be used [50]. Additionally, native microbial communities present in biofiltration usually lack the
necessary metabolic capabilities to completely degrade pollutants [49], [124]. Consequently,
leaving traces of pollutants or their degradation products being present in treated water [125].
The diversity of microbes present in biofilters is difficult to regulate because of the
characteristics of influent raw water. Hwang et al. [126] proved that pathogen strains dominate the
Schmutzdecke layer in their assessment of microorganisms and the Schmutzdecke of the bio sand
filter for the treatment of river water. Treating water using biological processes creates
opportunities for pathogenic contamination of potable water [62]. Furthermore, it is almost
impossible to achieve the presence of all effective pollutant removal in a single treatment system.
This necessitates the need to select microorganisms towards specific contaminants to improve the
effectiveness of biological water treatment.

39

2.7. Summary of reviewed literature
Although the use of biofiltration to treat water is promising, there are still areas that require
further research. There is a lack of detailed understanding of the mechanism of metabolic pathways
by which organic pollutants are degraded by microbes [71]. A microbial metabolic pathway is
dependent on the nature of microbes and their community in the biofilter bed. Further studies on
microorganism growth and degradation mechanisms are required to promote the implementation
of efficient biofiltration technology.
The effectiveness of biofiltration relies significantly on the type of microorganisms, their
community on the filter bed [70], [71], and microbial activities, which are temperature sensitive.
Extreme heat or cold events posed by climate change can harm the microbial communities in the
biofilter, thus decreasing the overall performance. Further research is critically needed on the
resilience of biofiltration under different conditions such as cold temperatures, sharp temperature
changes (e.g., sudden temperature change from warm to cold), and the study of the impact of raw
water quality shifts (due to climate change effect) on potable water treatment require further
studies.
In addition, the increase in temperature, posed by the changing climate, has led to increased
UVR exposure [127]. The carcinogenic effects of the UVR have resulted in the frequent use of
sunscreens to reduce the risk of skin cancer. Recently, UV filters, mostly used in cosmetics, are
being discharged into the environment in higher quantities [128]. The treatment of these
micropollutants has been futile, and they end up being discharged into source water bodies. There
is a need to develop advanced WTPs that can achieve high removal efficiency of these UV filters.
Research into the capability of biofiltration to remove these emerging contaminants is critically
required.

40

Raw water quality is altered after extreme weather events such as wildfires. This change affects
the concentration of pollutants and characteristics. An increase in contaminant levels leads to the
development of a thicker biofilm layer, which stops the diffusion of contaminants from reaching
the depth of the biofilm, thereby increasing the inactivity of microbial biofilm with different pore
sizes [129]. Although biofiltration excels at degrading certain pollutants, it may be less effective
in degrading higher concentrations of pollutants or new pollutants. Research on the optimal
thickness of the biofilm on the media is imperative to enhancing and maintaining the treatment
performance of biofilters.
Furthermore, it is crucial to observe the efficiency of the biofiltration system and develop an
operational code if a nature-based solution will be used to treat potable water [6]. To guarantee
optimal performance, biofiltration systems need regular maintenance and monitoring. This might
be difficult to maintain during severe weather events or when resources are limited. This brings
about the need to design and optimize techno-ecological natural-based solutions and operational
strategies.
In summary, this chapter has synthesized current knowledge on the impacts of climate change
on water quality and WTPs, emphasizing the growing challenges posed by extreme events such as
wildfires and rising temperatures. These phenomena increased UVR exposure to humans and
contaminants transportation into surface waters. Conventional treatment methods—including
coagulation, flocculation, AOPs, and filtration—have demonstrated varying degrees of
effectiveness in removing emerging contaminants, though they often entail high operational costs
and generate secondary waste. Biofiltration has emerged as a promising, sustainable alternative,
leveraging natural microbial processes to treat raw water. Its adaptability to diverse climatic
conditions and its ability to support microbial activity under stress highlight its potential as a

41

resilient treatment strategy. The successful implementation of biofiltration depends on careful
system design, appropriate media selection, and consistent operational monitoring. Despite its
limitations, biofiltration stands out as one of the most environmentally friendly and cost-effective
water treatment technologies. Advancing this approach could significantly enhance the resilience
and sustainability of water treatment systems in the face of ongoing climate change.

42

Chapter 3 Materials and Methods
3.1. Wildfire-impacted raw water preparation
The baseline raw water was reverse osmosis water collected from the University of Northern
British Columbia Enhanced Forestry Laboratory (EFL). Subsequently, wildfire-impacted raw
water was simulated by amending the baseline raw water using 0.5 g/L of bottom ash collected
from the University of Northern British Columbia Bioenergy plant. The DOC levels of the
wildfire-impacted raw water were adjusted using 0.938 g/L sodium acetate (C₂H₃NaO₂) and 0.208
g/L sodium propionate (NaC3H5O2) up to three levels: 20 mg/L, 50 mg/L, and 100 mg/L. The
addition of bottom ash, C₂H₃NaO₂, and NaC3H5O2 represented the severity of the disturbance and
the resulting degradation in source water quality [6].
The bottom ash was initially mixed with 10 L of baseline raw water for a period of 24 – 48 h
at a rate of 100 m/h to ensure water extractable organic matter was adequately leached from the
ash. Following mixing, the mixture was then introduced to the 200 L feed tank. The comprehensive
water quality of the influent can be found in Appendix A.
3.2. Pre-treatment
The wildfire-impacted water was pretreated using aeration and roughing filtration (Figure 3.1).
Diffused aeration introduces oxygen into the water in a fine bubble form, which promotes the
oxidation of organic matter, making them more amenable to removal in the subsequent treatment
stages. Diffused aeration replicates cascade aeration which is more cost effective on a large scale
[130]. Following aeration, wildfire-impacted raw water passed through rouging filters to reduce
the turbidity to <10 NTU. The rough filtration technology is effective in removing the larger
particles of ash, charred debris, and other suspended solids that are common in water sources
affected by wildfires [6]. These methods are simple, cost effective, and require minimal

43

maintenance. Removal of altered NOM, measured as DOC, is a common water treatment challenge
faced after a wildfire event [6].

Figure 3.1: Roughing filter set-up
3.3. Biofilter design and operation
Three 3-L bench-scale biofilters were set up in the EFL and run for 11 months. The biofilter
columns had an inner diameter of 7 cm and an estimated bed depth of 70 cm. For this research,
sand was selected as the biofilter media with an effective size of 0.475 mm and a uniformity
coefficient of 1.26, along with GAC with an effective size of 0.9 mm and a uniformity coefficient
of 1.88. Apart from the fact that both biofilter media are commonly used, they are also well known
for their high pollutant removal efficiency, affordability, and versatility [82], [83], [84], [85]. The
filters were operated in a downflow mode for 242 d during biofilter acclimation and 93 d during
the experimental periods. Each experimental run lasted for 30 d, and the biofilters were
backwashed for 24 h after each run. At the early phase of the acclimation period (168 d), the benchscale biofilters were designed with flowrate of 0.053 L/s (EBCT of 15 mins and HLR: 2.8 m/h).
Due to water logging in the biofilters caused by biofilms thickening, the flowrate was reduced to
44

0.018 L/s (EBCT: 56 min and HLR: 0.75 m/h). Throughout the entire experimental period, the
biofilters were maintained at a room temperature of 18 – 23 °C. Figure shows the water treatment
system used for this study.

Figure 3.2: Full biofiltration-based water treatment setup
3.4. Experimental design
Nine experimental runs with three varying levels of filter media (non-numerical factor) and
three varying levels of DOC (numerical factor) were conducted to examine the individual and
combined effects of the filter media type and DOC concentration in the raw water on DOC
removal. Specifically, these experimental runs allowed the investigation of every possible
combination of the independent variables of media type and DOC concentration measured as
chemical oxygen demand (COD) at room temperature (18 – 23℃). Three levels of DOC
concentration (20 mg/L, 50 mg/L, and 100 mg/L) and biofilter media (sand, GAC, and the
45

combination of sand and GAC) were observed. The dependent variable was the DOC removal
efficiency, enabling a comprehensive analysis of how these variables interact and influence the
DOC removal process. Table 3.1 outlines the experimental factors and their combinations,
allowing for a thorough analysis of each condition.
Table 3.1: The experimental runs based on biofilter media type and DOC concentration as
the factors
RUN

BIOFILTER MEDIA

DOC LEVEL (mg/L)

R1

Sand

20

R2

GAC

20

R3

GAC + Sand

20

R4

Sand

50

R5

GAC

50

R6

GAC + Sand

50

R7

Sand

100

R8

GAC

100

R9

GAC + Sand

100

3.5. Analytical methods
Performance testing was conducted on parameters such as pH, DO, UVA254, temperature,
COD, and alkalinity every 3 d (except for alkalinity) to determine the performance of each
biofilter. ATP and EPS were tested every other week to examine the presence and quantity of
microbes present in each biofilter. In addition, biodegradable dissolved organic carbon (BDOC)
of the feed water was measured to determine the quantity of DOC that can be degraded. All tests

46

were done in triplicate. Influent and pretreated water samples were collected from the top of the
tanks, while biofilter effluents were collected from the bottom of the biofilters. Additionally, media
samples for EPS and ATP were collected from the top of the biofilters. The procedures are outlined
below.
3.5.1. Dissolved oxygen (DO) and temperature
DO and temperature of feed water, pretreatment and biofilter effluents were measured using
the Thermo Scientific OrionTM Star A213 benchtop RDO/DO meter (Beverly MA). The RDO
optical dissolved oxygen probe was rinsed and wiped dry with a lint-free tissue and placed into the
collected sample. DO and temperature measurements were recorded upon stabilization of the
stirrer probe and display showing ‘ready’.
3.5.2. Turbidity
Turbidity was determined using Orion™ AQUAfast AQ3010 turbidity meter (Thermoscientific, Beverly, MA), The sample vial was rinsed and filled with the samples, wiped dry with
lint-free tissue and placed in the sample well of the turbidity meter. Readings that appeared on the
display of the turbidity meter were recorded in NTU.
3.5.3. pH
The pH of the samples was determined using DPH 7011 Digital pH Meter (General Tools and
Instruments, New York, USA). Prior to measurement, the pH meter probe was rinsed and dried
with a lint-free tissue. The probe was then placed in a beaker containing the samples. To ensure
uniformity, the samples were stirred continuously until a stable reading appeared on the pH meter.
The pH value was recorded once the reading stabilized.

47

3.5.4. Alkalinity
Alkalinity was measured using TNT870 test kits using Method 10239 (Hach, Canada). 2.0 mL
of solution A from the reagent set is added to the test vial. Subsequently, 0.5 mL of each sample
was added to the test vial. The vial was mixed thoroughly and left to react for 5 min. After reaction,
the vial was cleaned using a lint-free tissue and placed into the cell reader. Results were taken in
mg/L using a spectrophotometer.
3.5.5. Ultraviolet absorbance at 254 nm (UVA₅₂₄)
The UV-absorbing (UV-254) organics compounds present in the feed and effluent water was
measured every 3 d using standard method 5910 [131]. Samples were filtered using a 0.45-µm
filter and a vacuum filtration pump. The filtered samples were poured into a sample cell (2624410),
and UV absorbance was measured using a Hach DR 6000 spectrophotometer (Loveland, CO) at a
wavelength of 254 nm. The results were taken in cm-1. Then SUVA₅₂₄ was calculated from
Equation 3.1 [132].
SUVA₅₂₄ was measured using equation
𝑺𝑼𝑽𝑨 =

𝐔𝐕 𝐚𝐛𝐬𝐨𝐫𝐛𝐚𝐧𝐜𝐞 (𝒄𝒎−𝟏 )
𝐃𝐎𝐂 (𝐦𝐠/𝐋)

* 100………………….……. Equation 3.1

3.5.6. Extracellular polymeric substances (EPS)
For EPS extraction [133], [134], 10 mL of each biofilter media was collected and put inside a
50 mL vials. Reverse osmosis (RO) water was filtered through a 0.45- µm filter and added to the
50 mL vials up till the 50 mL mark. 0.3 mL of filtered 37 % formamide was added to the vials.
The vials were placed in an orbital shaker at a speed of 30 rpm for 1 h. After 1 h, the sample was
centrifuged at 5000 g for 15 min at 4 °C. The supernatant was first filtered through a 0.45- µm
filter, then 0.22- µm filter. The filtrate was labelled loosely bond EPS (LB-EPS).

48

To extract tightly bond EPS (TB-EPS), the sediments from LB-EPS was resuspended by
adding filtered extraction buffer to 50 mL mark. The pH of the suspension was adjusted to
approximately 11 by adding 0.4 mL of 1mol/L of NaOH and placed in an orbital shaker for 3 h.
After 3 h, the sample was centrifuged at 10000 g for 15 min at 4 °C. The supernatant was first
filtered through a 0.45- µm filter, then 0.22- µm filter. The filtrate was labelled tightly bond EPS
(TB-EPS).
To quantify EPS sugar, the colorimetric method for determination of sugars and related
substances was used [135]. In a 250 mL amber glassware, 5g of phenol crystals was added to 100
mL of RO water. Four concentrations of glucose solutions were prepared by diluting 5.6 g/L of
glucose stock solution. In a 15 mL test tubes, 1 mL of the different concentrations of glucose
solution were added followed by 1 mL of 5% phenol, and 5 mL of H2SO4. A blank was prepared
by adding 1 mL of RO water, 1 mL of 5% phenol, and 5 mL H2SO4. The test tubes were sealed
and left to react for 10 min. After 10 min, the test tubes were placed on the INTTLAB vortex mixer
VM370 for 10–15 s before placed into the Labfish digital water bath LFUS034 for 20 min at 30°C.
The test tubes were wiped, and results were recorded using a spectrophotometer at a single
wavelength of 490 nm. A calibration curve was obtained from the results and is attached in
Appendix A.
For the quantification of LB-EPS, the same procedure was repeated except that 1 mL of sample
was mixed with 1 mL of 5% phenol, and 5 mL H2SO4 while the sample for TB-EPS was diluted
with dilution water (DW) to get a factor of 5. The final sugar concentrations were calculated using
the line equation from the calibration curve obtained.
For the quantification of protein, the modified Lowry method was used. Solution A was
prepared by dissolving 20g of Na2CO3 and 4g of NaOH into 800 mL RO water. More RO water

49

was added to bring the solution to 1000 mL. Another solution B1 was prepared by dissolving 1g
of CuSO4.5H2O into 60 mL of RO water, and more RO water was added to bring the solution to
100 mL. Following this step, solution B2 was prepared by adding 2g of C4H4KNaO6.4H2O in 60
mL RO water. More RO water was added to bring the solution to 100 mL. To prepare solution C,
50 mL of solution A, 0.5 mL of solution B1, and 0.5 mL of solution B2 were mixed. This solution
was prepared on the day of analysis and used while still fresh. A final solution E was prepared in
an amber glassware by mixing Folin-Ciocalteu reagent with RO water in a 1:1 ratio.
For calibration, Bovine Albumin Serum (BSA) standard solution of concentrations 0, 20, 40,
60, 80, and 100 mg/L were prepared. A 200 mL BSA stock solution was made by adding 0.2g
BSA and 1.8g NaCl to 100 mL RO water. The volume was completed by adding RO water until a
200 mL mark. For concentrations 20, 40, 60, 80, and 100 mg/L, 0.5, 1.0, 1.5, 2.0, and 2.5 mL of
BSA stock solutions were added and placed into 50 mL vials, respectively. RO water was added
to each vial up to the 25 mL mark. For 0 mg/L (blank) standard, RO water was used. In 15 mL test
tubes, 1 mL of each concentration standard was mixed with 3 mL of solution C and 0.3 mL of
solution E. The test tubes were closed, and the tubes were vortexed for about 10 s. Samples were
placed in a light-protected foam box and left to react for 2 h. After the reaction, results were
recorded using a spectrophotometer at a single wavelength of 750 nm. A calibration curve was
obtained from the results and is attached in Appendix A. The calibration procedure was repeated
for the extracted EPS samples. For the quantification of LB-EPS, 1 mL of raw samples was used
while the samples for TB-EPS were diluted with DW to get a factor of 5. The final protein
concentrations were calculated using the linear equation from the calibration curve obtained.

50

3.5.7. Adenosine triphosphate (ATP)
ATP was analyzed using the Invitrogen ATP determination kit [136]. To prepare the reagents,
the following steps were followed. 50 µL of 20X reaction buffer (component E) was added to 950
µL of dH2O to make 1 mL of 1X reaction buffer. To make 1 mL of 10 mM ᴅ-Luciferin stock
solution, 1X reaction buffer was then added to one vial of ᴅ-Luciferin (component B, MW 302,
blue cap). Following this step, 100 mM Dithiothreitol (DTT) stock solution was made by adding
1.62 mL of dH2O to 25 mg of DTT (component C, MW 154, black cap). The DTT stock solution
was aliquoted into ten 160 µL volumes and frozen at ≤ -20°C. Finally, 10 mL of several
concentrations of ATP standard solutions (1 µM, 2 µM, 5 µM, and 10 µM) were prepared by
diluting the 5 mM ATP solution (component D, green cap) in dH2O. The diluted ATP solutions
were aliquoted into 2 mL and stored frozen at ≤ -20°C.
In a 15 mL vial, 10 mL of the standard reaction solution was made by adding 8.9 mL of dH2O,
0.5 mL of 20X reaction buffer, 0.1 mL of 100 mM DTT, 0.5 mL of 10 mM ᴅ-Luciferin, and 2.5
µL of firefly luciferase 5 mg/mL stock solution (component B, red cap). The vial was inverted to
mix the solution and kept away from light.
In a 96-well plate, 100 µL of the standard reaction solution was added to wells A1, B1, and
C1. In wells A2 – C5, the reaction was started by adding 90 µL of the standard rection solution
and 10 µL of the different concentrations of ATP standard solutions and biofilter media samples.
On the Gen 5 protocol software, an experiment was created. 96 well plate type and luminescence
detection method were selected. On the plate layout, each well was labelled according to their
content. Following this procedure, the plate was placed in the BioTek Synergy 2 microplate reader
(Edmonton, Canada) and the plate was read. A standard curve for the series of ATP concentrations

51

was generated and used to estimate the ATP concentration of microbes present in the biofilter
media. Standards were run with each experiment due to the decreasing sensitivity of the assay.
3.5.8. Biodegradable dissolved organic carbon (BDOC)
BDOC was analyzed using the method developed by Khan et al. [137] and the standard method
for BOD [131]. Sample was collected and analyzed within 2 h. Prior to analysis, sample’s
temperature was adjusted to 20 ± 3 °C by placing it in the incubator and filtered through a 0.45µm filter. The DW was prepared immediately before use by adding 1 mL of each phosphate buffer,
MgSO4, CaCl2, and FeCl3 to 1 L of dH2O of DO ≥ 7.5 mg/L. In addition, one capsule of Polyseed
(InterLab, Texas, USA) was added for every 500 mL of dH2O. The mixture was thoroughly mixed
using a JLT6 Flocculator Tester (Usmate, Italy) at a speed of 200 rpm for 60 mins. Following this
step, the mixture was left to settle for 15 mins, supernatant was extracted, and temperature was
adjusted to 20 ± 3 °C.
Using DW, three dilutions of samples i.e., 25:75 (% DW: % sample), 50:50 and 75:25 were
prepared. This was estimated to produce at least one dilution with DO ≥ 1 mg/L and DO uptake of
2 mg/L or more at the end of a 5-d incubation period. Following dilution, 300 mL of each dilution
were poured into BOD bottles. A seed control (sample s) was prepared using the same method
except that it was 100% DW. All prepared samples were analyzed for DO and recorded. After DO
measurement, BOD bottles were incubated in a Fisherbrand Isotemp BOD refrigerated incubator
at a temperature of 20 ± 1 °C for 5 d. Additionally, samples from each dilution were collected for
COD analysis.
After the 5-d period, BOD bottles were removed from the incubator. The samples were
analysed for DOf DOsf, DOCsf, and DOCf. Of all the dilutions, only the dilution 50-50 had DO ≥

52

1 mg/L and DO uptake of 2 mg/L, therefore the dilution ratio was used to calculate the dilution
factor, F, using the equations 3.5-1 to 3.5-3:
𝑭=

𝒗𝒐𝒍𝒖𝒎𝒆 𝒐𝒇 𝒅𝒊𝒍𝒖𝒕𝒊𝒐𝒏 𝒘𝒂𝒕𝒆𝒓 (𝒎𝑳)+𝒗𝒐𝒍𝒖𝒎𝒆 𝒐𝒇 𝒔𝒂𝒎𝒑𝒍𝒆(𝒎𝑳)
𝒗𝒐𝒍𝒖𝒎𝒆 𝒐𝒇 𝒔𝒂𝒎𝒑𝒍𝒆 (𝒎𝑳)

…………….. Equation 3.2

BOD5 and BDOC were calculated using the following equations:
𝑩𝑫𝑶𝑪 = [(𝑫𝑶𝑪𝒊 − 𝑫𝑶𝑪𝒇 ) − (𝑫𝑶𝑪𝒔𝒊 − 𝑫𝑶𝑪𝒔𝒇 )] ∗ 𝑭……….….… Equation
3.3
𝑩𝑶𝑫𝟓 = [(𝑫𝑶𝒊 − 𝑫𝑶𝒇 ) − (𝑫𝑶𝒔𝒊 − 𝑫𝑶𝒔𝒇 )] ∗ 𝑭 …………................ Equation
3.4
Observing the criteria of DOf ≥ 1 mg/L and (DOi – DOf) ≥ 2 mg/L [131].
3.5.9. Dissolved organic carbon (DOC)
The DOC concentration was measured in terms of chemical oxygen demand (COD). The COD
was determined according Standard Methods 5220D using low range (≤ 90 mg/L) COD solution
[131] . This standard COD solution contained 1.2 mL/tube of digestion solution and 2.8 mL/tube
of sulfuric acid reagent. To prepare the digestion solution, 1.022 g of potassium dichromate
(K2Cr2O7) was added to 500 mL of deionized water (dH2O). 167 mL of concentrated sulfuric acid
(H2SO4) and 33.3 g of mercuric sulfate (HgSO4) were added to the K2Cr2O7 solution and allowed
to cool to room temperature. After cooling, the solution was diluted to 1000 mL by adding dH2O.
This digestion solution was stored in a 2L pyrex storage glass bottle. The dichromate ion (Cr2O72) from K2Cr2O7 oxidizes the COD present in the sample. To prepare the sulfuric acid reagent, 10.1
g of silver sulfate (Ag2SO4) was added to 1000 mL of H2SO4 and allowed to dissolve for 24–48 h
before mixing. Ag2SO4 ensures thorough oxidation of organic matter, while H₂SO₄ helps suppress

53

interference from compounds like bromine, chloride, and iodide, maintaining accurate processing
and results.
After mixing, 1.2 mL of the digestion solution and 2.8 mL of the sulfuric acid reagent were
pipetted into 15 mL test tubes. The test tubes were capped and thoroughly mixed for homogeneity.
To evaluate the standard solutions, different volumes of potassium hydrogen phthalate stock and
dH2O were added to the prepared COD solution. The test tubes were capped and thoroughly mixed
to achieve a homogeneous mix before placing into a pre-heated (150°C) Hach DRB 200 COD
digester. The test tubes were left in the digester for 2 h at a temperature of 150°C. They were left
to cool to 120°C before taken out of the digester and left to cool again at room temperature (20 –
25°C). Results were recorded using a spectrophotometer at a single wavelength of 420 nm. A
calibration curve was obtained from the results and is attached in Appendix A.
To determine the total COD concentration of the samples (influent and effluents), 2 mL of
filtered sample (through a 0.45- µm filter) was added to the prepared COD solution and capped.
The test tubes were capped and thoroughly mixed to achieve a homogeneous mix before placing
into a pre-heated (150°C) Hach DRB 200 COD digester (Loveland, CO). The test tubes were left
in the digester for 2 h at a temperature of 150°C. They were left to cool to 120°C before taken out
of the digester and left to cool again at room temperature. Results were recorded using a Hach DR
6000 spectrophotometer (Loveland, CO) at a single wavelength of 420 nm. The final COD
concentration was calculated using the line equation from the calibration curve obtained. For
influent COD concentration of 150 mg/L (DOC = 50 mg/L), 1 mL of sample was diluted with 1
mL of dH2O to obtain a dilution factor of 2. Similarly for an influent COD concentration of 300
mg/L (DOC = 100 mg/L), 0.5 mL of sample was diluted with 1.5 mL of dH2O to obtain a dilution
factor of 4.

54

3.6. Statistical analyses
To evaluate the influence of DOC concentration and media type on DOC removal, a
comprehensive statistical analysis was conducted. Initially, an outlier test was performed to
identify and address any extreme values that could skew the results. This was followed by a
normality test to assess the distribution of the data, ensuring the appropriateness of subsequent
parametric or non-parametric analyses. Given the nature of the data, a Spearman correlation test
was employed to examine the monotonic relationships between DOC removal and the predictor
variables. To further explore the predictive capacity of the variables, a fit model regression analysis
was conducted, incorporating media types and initial DOC concentration as predictors. Finally, an
analysis of variance (ANOVA) was used to determine the statistical significance of the differences
in DOC removal across the different media types. A significance level of p ≤ 0.05 was used to
determine the rejection of the null hypothesis, indicating a statistical difference between the
analysed data groups. This multi-faceted approach provided a robust framework for understanding
the key factors affecting DOC removal efficiency.

55

Chapter 4 Results and Discussion
4.1. Influent water quality
Detailed influent water quality analysis is presented in Appendix B. The water sample was
generally within the acceptable limits set by the Guidelines for Canadian Drinking Water Quality
(GCDWQ) for most parameters; however, several exceeded the recommended operational
guidelines (OG) or aesthetic objectives (AO). Notably, the concentration of aluminum was
measured at 0.23 mg/L, exceeding the OG of 0.10 mg/L, and manganese’s concentration was 0.24
mg/L, surpassing the AO of 0.02 mg/L. Iron had a concentration of 0.44 mg/L, above its AO limit
of 0.30 mg/L. The pH level of 9.32 exceeded the recommended range of 6.50 to 8.50, potentially
affecting disinfection efficiency and corrosion control. Additionally, turbidity at 14 NTU and color
at 80 TCU were significantly above their respective AO of ≤1.0 NTU and ≤15 TCU, which may
impact taste, appearance, and consumer acceptability.
In contrast, total dissolved solids at 293 ppm and sodium at 77 mg/L were within acceptable
ranges, with the AO for sodium being ≤200 mg/L. Microbiological analysis showed no presence
of E. coli or total coliforms, indicating no immediate microbial risk. However, the heterotrophic
plate count (HPC) was high >2420 MPN/mL, exceeding the general guidance level of 500
MPN/mL, which may suggest the presence of non-pathogenic microbial activity. Overall, while
the water is microbiologically safe, several physical and chemical parameters exceed OG or AO,
indicating a need for further treatment or monitoring to ensure consumer acceptability and system
performance.
4.2. Temperature
The mean temperature across runs and treatment systems ranged between 19°C - 21°C (Figure
4.1). Moll et al. [138] noted a higher removal performance (42%) when biofilters operated at higher

56

temperatures (20 and 35°C), compared to lower temperatures (5°C). Similarly, in a study
investigating the removal of assimilable organic carbon (AOC) using a dual media, Price et al.
[139] found no AOC removal at temperatures less than 16°C. However, when operated above
16°C, the system achieved an average AOC removal that was 44% higher. Although the present
study did not include operations at lower temperatures, the observed DOC removal rate aligns with
findings from studies conducted at comparable temperatures.

Figure 4.1: Mean temperature of influents and effluents at DOC = 20 mg/L (a) 50 mg/L (b),
and 100 mg/L (c)
4.3. Turbidity
Turbidity varied throughout the experimental period. At influent DOC concentration of 20
mg/L (Figure 0a), the turbidity of the influent water remained relatively high with noticeable
peaks around days 14 and 28, indicating inconsistent water quality before treatment. The effluent
from pretreatment system and biofilters all maintained consistently low turbidity levels. An

57

average of 76 ± 13% (roughing filter), 78 ± 19% (sand), 82 ± 13% (GAC), and 83 ± 9% (sand +
GAC) turbidity removal demonstrated effective and stable filtration performance. Among the
treatment systems, sand + GAC biofilter appears to provide the most consistently low effluent
turbidity, indicating that it is the most effective at reducing particulate matter.
At the influent DOC concentration of 50 mg/L (Figure 4.2b), influent water turbidity starts
with a sharp spike in turbidity, peaking on day 6 at (85 NTU), indicating a high level of suspended
particles in untreated water. After day 6, turbidity drops sharply, due to natural settling. The
treatment system maintained consistently low turbidity levels, averaging 74 ± 21% (roughing
filter), 80 ± 15% (sand), 77 ± 20% (GAC), and 78 ± 18% (sand + GAC) turbidity
removal throughout the 30-d period. This suggests that the filtration methods are highly effective
at removing particulates and maintaining water clarity. Similar to influent DOC concentration of
20 mg/L (Figure 4.2a), sand + GAC biofilter is the most efficient due to its dual-stage filtration.
The untreated influent for influent DOC concentration of 100 mg/L (Figure 0c), consistently
showed the highest turbidity, serving as a baseline. The roughing filter moderately reduces
turbidity, while sand filtration performs better with a gradual decline. GAC shows a more
significant reduction, and the Sand + GAC biofilter achieved the lowest turbidity levels throughout
the period. Throughout the 30 d, the treatment system consistently achieved high turbidity removal
efficiencies, averaging 69 ± 16% with the roughing filter, 72 ± 26% with sand filtration, 71 ± 23%
with GAC, and 75 ± 22% with the sand + GAC biofilter. This indicates that the Sand + GAC
biofilter is the most effective in improving water clarity, with all treatments showing consistent
trends supported by error bars indicating measurement variability.

58

Figure 0: Trends of turbidity reduction at DOC = 20 mg/L (a), 50 mg/L (b), and 100 mg/L (c)

59

The turbidity result observed in this study is consistent with previous literature. Blackburn et
al. [6] observed effluent turbidity reduced to ≤ 0.3 NTU in 93% of biofilter effluent samples
analyzed. Similarly, the filtrate turbidity of biofilters used as pretreatment to membrane-based
desalination had a turbidity value of 0.2 – 0.3 NTU and 0.28 – 0.31 NTU for anthracite and GAC
biofilters, respectively [140]. Although the turbidity levels in the biofilters’ effluent are higher than
those reported in previous studies and exceed the GCDWQ operational guideline of ≤ 1 NTU, the
results still highlight the effectiveness of biofiltration in reducing turbidity.
4.4. pH
The pH results of the influent and the biofilters at all concentrations are shown in Figure 0.
The average influent pH was 9.38 ± 0.25, 9.40 ± 0.25, and 9.52 ± 0.12 for influent DOC
concentrations of 20 mg/L, 50 mg/L, and 100 mg/L, respectively. At 20 mg/L influent DOC
concentration (Figure 0a), there was a drop in pH across the treatment system. For the first 14 d,
the biofilter effluent had pH ranging from 7.42 – 8.83. The pH of the biofilter effluent increased
(8.95 – 9.53) for the next 12 d before finally dropping to a range 8.37 – 8.83. At 50 mg/L influent
DOC concentration (Figure 0b), the pH showed a slight decline during the first 12 d, followed by
a sharp drop over the next 12 d. During the final 6 d, pH levels remained relatively stable with
minimal change. For 100 mg/L influent DOC concentration (Figure 0c), the starting pH for the
GAC and sand + GAC biofilters were below 8.5 for the first 6 d. In the subsequent days, the pH
of all biofilters increased to > 9. Generally, the average pH of effluents at 20 and 50 mg/L influent
DOC concentration aligns with the ideal pH range of biofiltration systems [1], [28], [29]. However,
the effluents from runs experimental runs at 100 mg/L influent DOC concentration exceeded the
ideal pH range of biofiltration systems.

60

Sodium acetate and sodium propionate added to the influent water contributed to its alkaline
properties. Sodium acetate dissociates into sodium ions (Na+) and acetate ions (CH3COO-) when
it dissolves in water. The CH3COO- then reacts with water to form acetic acid and hydroxide ions
(OH-). The resulting OH- makes the pH more basic by increasing the solution’s pH. Similarly,
sodium propionate dissociates into Na+ and propionate ions (CH3CH2COO-) when it dissolves in
water. The CH3CH2COO- then react with water, producing propionic acid and OH-. The OH- that
is produced raises the pH.

61

Figure 0: Trends of pH of influents and effluents at DOC = 20 mg/L (a) 50 mg/L (b), and 100 mg/L (c)

62

4.5. Alkalinity
All treatments at different DOC concentrations show non-linear, fluctuating trends in alkalinity
concentration over time (Figure 0). The values range from 259 to 1470 mg/L, indicating
remarkable variability. Alkalinity levels of runs at 20 mg/L influent DOC concentration (Figure
0a) dropped sharply after day 20 before rising again. In contrast, runs at influent DOC
concentration of 50 mg/L (Figure 0b) showed a continuous increase in alkalinity up to day 30.
Runs at 100 mg/L influent DOC concentration (Figure 0c) exhibited significant fluctuations, with
pronounced peaks and dips. The elevated alkalinity in the treatment effluents suggests a buffering
effect, which may explain the resistance to changes in pH. Hamidi et al. [141] reported that changes
in alkalinity had a modest impact on biofilter performance in removing TOC and ammonia, as well
as ATP levels. Additionally, the GCDWQ have no aesthetic objective (AO) or maximum
acceptable concentration (MAC) for alkalinity. Thus, proving that treated water with high
alkalinity is consumable.

63

Figure 0: Trends of alkalinity of influents and effluents at DOC = 20 mg/L (a) 50 mg/L (b), and 100 mg/L (c)

64

4.6. Ultraviolet absorbance at 254 nm (UVA₅₂₄) and specific ultraviolet absorbance at 254nm
(SUVA₅₂₄)
UVA₅₂₄ results showed notable variation across the different treatment runs. In runs at influent
DOC concentration of 20 mg/L (Figure 3.0a), the influent exhibited a consistent and steep increase
in UV absorbance, particularly after day 10, indicating a rising concentration of organic matter.
The roughing filter showed a gradual increase, while the sand biofilter started with low absorbance
and steadily increased, peaking between days 20 and 30. The GAC biofilter began with high
absorbance and remained relatively stable, while the Sand + GAC biofilter showed a moderate
increase, especially after day 10. Runs with influent DOC concentration of 50 mg/L (Figure 3.0b)
followed similar patterns: the influent maintained a clear upward trend with less fluctuation, the
roughing filter showed a gradual rise, and the GAC biofilter remained stable with minor variations.
Both the sand and Sand + GAC biofilters displayed increasing trends, with the latter showing
slightly more variability.
In runs with influent DOC concentration of 100 mg/L (Figure 3.0c), the influent consistently
had the highest UV absorbance, reflecting a high organic load. The roughing filter and sand
biofilter achieved moderate reductions, with sand biofilter performing slightly better over time.
Sand biofilter significantly lowers UV absorbance and maintains stable performance. GAC
demonstrated a significant and sustained increase in UV absorbance, while the Sand + GAC
biofilter achieved the lowest values from day 12 – 21, indicating the most effective removal.
Overall, the sand biofilter proved most effective in reducing UV-absorbing organic compounds.
However, a notable increase in UV absorbance is observed across all biofilters after day 18,
suggesting a potential decline in filtration efficiency or a change in influent water quality during
that period.

65

Figure 3.0: UVA254 trends for DOC at 20 mg/L (a), 50 mg/L (b), and 100 mg/L (c)

66

Blackburn et al. [6] reported a difficulty in reducing UVA254 which was consistent with the
findings of this study and other previous research [72], [97], [142]. Due to the treatment
mechanism of biological systems, a significant reduction in UVA254 was not anticipated. This is
associated with the fact that UVA, especially at the wavelength of 254nm, is directly proportional
to the concentration of organic matter in water. It indicates the presence of DOC [72], thus proving
the presence of lower DOC concentration in the biofilter effluent.
SUVA254 was calculated by dividing UVA254 by the relative DOC concentration. SUVA254
characterizes the aromatic nature of organic matter in water [143]. A higher SUVA254 value
indicates a higher proportion of aromatic compounds such as humic substances, which indicate a
greater potential for the formation of disinfection byproducts (DBPs) when treated with chlorine.
The influent and effluents at all DOC concentrations had SUVA254 values < 2 L/mg.m throughout
the experimental period (Figure 4.6). This indicates that the influent water, roughing filter, and
biofilters effluents had mostly hydrophilic and low molecular weight compounds in its NOM
composition [132]. Additionally, the potential TOC removal for SUVA254 < 2 L/mg.m is 0 – 40%,
2 – 4 L/mg.m is 40 – 60%, and > 4 L/mg.m is 40 – 60%.

67

Figure 4.6: Average SUVA of influents and effluents at DOC = 20 mg/L (a) 50 mg/L (b),
and 100 mg/L (c)
4.7. Dissolved Oxygen (DO)
The average DO of the influent water, pre-treated and biofilter effluents, measured at different
runs is illustrated in Figure 4.7. A decrease in DO was observed across the treatment system with
the influent water having the highest average DO level (6 ± 1 mg/L; 7 ± 3 mg/L ; and 7 ± 0 mg/L)
while the roughing filter effluent had the lowest (2 ± 1 mg/L; 3 ± 1 mg/L ; and 4 ± 1 mg/L) at DOC
concentrations 20 mg/L, 50 mg/L, and 100 mg/L. These findings are consistent with Nemani et al.
[144], who recorded influent DO ranging from 7 mg/L to 8 mg/L while evaluating the impact of
biofilter operation on microbial community structure and performance, which correspond to the
expected saturation levels of dissolved oxygen in water.
Reduction in influent DO water during biofiltration systems is primarily due to the biological
activity within the filter. Biofiltration systems designed for the removal of BOM operates under

68

aerobic conditions, with oxygen serving as the terminal electron acceptor for heterotrophic
microorganisms [145]. The growth of biofilms on the filter media involves microbial activity such
as respiration and it consumes oxygen. As these biofilms develop and mature, they continue to use
oxygen from the water [13], [146]. Therefore, the rate of oxygen consumption is a direct indicator
of microbial activity related to DOC removal [145]. Gomez et al. [147] observed that DO affects
biofilm development, with excessive DO levels inhibiting biofilm growth. Thus, the decrease in
DO indicates microbial activity and biofilm formation within the roughing filter and biofilters.
Furthermore, the interdependence of oxygenation and pollutant removal (manganese and
ammonium) in drinking water biofilters was established [148].

Figure 4.7: Mean DO levels of influents and effluents at DOC = 20 mg/L (a) 50 mg/L (b),
and 100 mg/L (c)
4.8. Adenosine triphosphate (ATP)
In runs at influent DOC concentration of 20 mg/L at day 30 (Figure 4.8a), the GAC biofilter
shows the highest ATP concentration (1.1 ± 0.1 mM), indicating the most active microbial
community among the three media. The sand biofilter follows with a moderate ATP level (1.0 ±
69

0.1 mM), while the sand + GAC biofilter has the lowest ATP concentration (0.8 ± 0.1 mM),
suggesting limited microbial activity. Runs at influent DOC concentration of 50 mg/L shows a
different trend. On both day 17 and day 26 (Figure 4.8a), the sand biofilter consistently exhibits
the highest ATP concentrations (14.4 ± 1.8 mM, 6.5 ± 1.3 mM), reinforcing its effectiveness in
sustaining microbial growth. GAC biofilter maintains moderate levels (4.5 ± 0.7 mM, 2.6 ± 0.5
mM), while sand + GAC biofilter remains the least biologically active (2.2 ± 0.6 mM, 1.5 ± 0.4
mM). The trend suggests that microbial activity decrease over time which could result to decrease
in pollutant removal.

Figure 4.8 ATP concentration at steady state (a) and Average DO concentration (b) of all
runs
In run 7 – 9 with influent DOC concentration of 100 mg/L, a significant drop in ATP
concentration was observed after day 10 and slightly peaked on day 26. On day 10, the highest
ATP concentrations of the sand (4.5 ± 0.4 mM) and GAC (1.9 ± 0.1 mM) biofilters were observed.
The highest ATP concentration of the GAC biofilters (1.4 ± 1.0 mM) was observed on day 26
(Figure 4.8a). The lowest ATP concentrations of all biofilters was observed on day 17; 0.8 ± 0.1
mM (sand), 0.8 ± 0.2 mM (GAC), and 0.6 ± 0.1 Mm (sand + GAC). Across all time points, sand
biofilter leads in microbial activity, with steadily increasing ATP levels. GAC shows a similar
upward trend but at lower concentrations, while sand + GAC remains consistently low except for
70

day 26. The data confirm that sand biofilter enhances and sustains microbial growth more
effectively than other biofilters.
Previous research observed that source water quality, EBCT, media type, and temperature do
not affect the ATP concentration at the acclimated biofilters surface [149], [150]. However, an
increase in influent DOC concentration and HLR influences ATP concentration [123], [150].
Additionally, this study identified a general trend of a negative correlation between DO and ATP
concentrations (Figure 4.8a & 4.8b). At each DOC concentration, the lowest DO levels typically
corresponded with the highest ATP concentrations, and vice versa – except in Runs 4 (sand at 50
mg/L) and 5 (GAC at 50 mg/L), which deviated from this pattern.
4.9. Extracellular polymeric substances (EPS)
The EPS proteins and sugar concentrations across experimental R1 – R9, distinguishing
between loosely-bound EPS(LB–EPS) and tightly-bound (TB–EPS) are shown in Figure 4.9. In
terms of protein concentration (Figure 4.9a), TB-EPS exhibited higher values compared to LBEPS in runs R1, R2, R3, and R6, while LB-EPS slightly surpassed TB-EPS in runs R4, R5, R7,
R8, and R9. The highest protein concentration in TB-EPS is observed in Run R6 (sand + GAC at
50 mg/L), suggesting a peak in microbial activity or biofilm development during that
phase. Throughout the experimental period, the highest concentrations of LB–EPS and TB–EPS
proteins were produced in R2 (GAC at 20 mg/L) of 6.6 ± 1.5 mg/g TSS and 21.0 ± 7.5 mg/g TSS,
respectively, suggesting a peak in microbial activity or biofilm development during that phase.
The lowest concentration of LB–EPS proteins observed was in R5 (1.5 ± 0.4 mg/g TSS).

71

Figure 4.9: EPS proteins (a), and sugar (b) concentration at each run
Similarly, in Figure 4.9b, which displays EPS sugar concentrations, LB-EPS dominates runs
R1, R2, R4 and R6, showing significantly higher values than TB-EPS. The sugar concentration in
TB-EPS also peaks in runs R3, R5, R7, R8, and R9. The highest concentration of LB-EPS and TBEPS sugar were produced in R7 (sand at 100 mg/L) of 262.3 ± 72.3 mg/g TSS and 295.6 ± 36.9
mg/g TSS, respectively. It is also worth noting that higher concentrations of sugar were produced
compared to proteins. The results highlight the critical role of TB-EPS in biofilm structure and
function, as it not only contributes more substantially to the EPS matrix but also reflects the
intensity of microbial processes within the biofilters.
The variation in EPS protein and sugar concentrations across different experimental runs can
be largely attributed to pH differences. The optimal pH range for EPS production is reported to be
between 5.50 and 6.50 [151], [152]. Although the pH in Run 2 (GAC at 20 mg/L) ranged from
7.42 to 8.62 – slightly above the optimal range – it was the lowest among all runs, which likely
contributed to the highest observed EPS protein production. In contrast, Shu and Lung [153]
reported that higher pH levels favored the production of low molecular weight exopolysaccharides
with higher yields. This aligns with the findings of this study, where Run 7 (sand at 100 mg/L)

72

exhibited the highest average pH (9.46 ± 0.17) and correspondingly produced the highest
concentration of EPS sugars.
EPS are produced by microbes in biofilters aiding in bacterial attachment, shielding them from
harsh environmental conditions [154]. Although, it is hard to numerically compare EPS due to
different extraction methods, however, Table 4.1 summarizes extracted EPS concentration from
previous literature and this study.
Table 4.1: Previously reported EPS concentration extracted from water biofilters
Media type

Extraction buffer

EPS Concentration

Units

References

Anthracite and

Cation Exchange Resin with

Proteins: 34 – 166

μg BSA/g wet media

[155]

sand

6 mM PBS

Sugar: 11 – 102

μg D-glucose/cm3 media

GAC

Cation Exchange Resin with

Proteins: 86.5 – 102.7

μg BSA/cm3 media

[154]

[156]

6 mM PBS
GAC, anthracite,

Formaldehyde (36.5%) and

Proteins: 1 – 5

μg BSA/cm3 media

and sand

NaOH (1 N)

Sugar: 0.5 – 3

μg D-glucose/cm3 media

GAC, anthracite,

10 mM Tris, 10 mM EDTA,

Proteins: 0.27 – 3.38

mg BSA/g TS

and sand

2.5% NaCl, pH 8

Sugar: 0.02 – 0.6

mg glucose/g TS

Sand and GAC

Formaldehyde (37%) and

Proteins: 3.38 – 27.63

mg BSA/g TSS

NaOH (1 M)

Sugar: 67.69 – 557.86

mg glucose/g TSS

4.10. Biodegradable dissolved organic carbon (BDOC) and dissolved organic carbon (DOC)
profile
BDOC, the average biodegradable fraction of DOC, of the influent water at all DOC
concentrations, ranged from 18 % to 72 %, and is summarized in Table 4.2.

73

[157]

This study

Table 4.2: BDOC concentration of influent water
Experimental Runs

DOC Concentration

Average BDOC Concentration

BDOC

(mg/L)

(mg/L)

(%)

R1 –R 3

20

11 ± 0

55

R4 – R6

50

26 ± 14

72

R7 – R9

100

18 ± 2

18

Figure 4.10 shows the DOC concentration of both influent and effluent at DOC = 20 mg/L (a),
50 mg/L (b), and 100 mg/L (c). Runs 1 – 3 (Error! Reference source not found. 4.10a) DOC c
oncentration reached steady state after 8 d. The biofilters achieved DOC removal efficiencies of
up to 16% (roughing filter), 80% (sand), 85% (GAC), and 83% (sand + GAC), averaging 51 ±
15% (roughing filter), 68 ± 22% (sand), 68 ± 16% (GAC), and 70 ± 20% (sand) at steady stage
(day 8 – 30) (Figure 4.1a).

b 100

80
60

68

70

68

DOC removal (%)

DOC removal (%)

a 100
51

40
20
0

80
60

47

40

54

33

25

20
0

Roughing Filter

Sand

GAC

Sand + GAC

Roughing Filter

Sand

GAC

c
DOC removal (%)

40

20

12

16

16

GAC

Sand + GAC

4
0
Roughing Filter

Sand

-20

Figure 4.1:Average DOC removal (%) of influents and effluents at DOC = 20 mg/L (a) 50
mg/L (b), and 100 mg/L (c)

74

Sand + GAC

In runs with influent DOC concentration of 50 mg/L (Figure 4.9b), DOC removal efficiencies
of up to 59% (roughing filter), 65% (sand), 78% (GAC), and 69% (sand + GAC), averaging 25 ±
18% (roughing filter), 33 ± 19% (sand), 47 ± 19% (GAC), and 54 ± 16% (sand) (Figure 4.1b).
The highest DOC removal was achieved on day 12 after which the removal efficiency dropped
significantly. This decrease was also noted in the ATP concentration (after day 17). Studies have
observed that ATP levels at the surface of the biofilter are not necessarily related to biofilter
performance in terms of DOC removal [79], [92], [150], [158]. However, a decrease in primary
energy carriers that break down organic matter across the experimental period aligns with the
reduction in DOC removal efficiency.
DOC removal efficiencies achieved in runs with influent DOC concentration of 100 mg/L
(Error! Reference source not found.c) were up to 27% (roughing filter), 58% (sand), 51% (
GAC), and 59% (sand + GAC). The average removal efficiencies as shown in Figure 4.1c, were
4 ± 11% (roughing filter), 12 ± 22% (sand), 16 ± 21% (GAC), and 16 ± 26% (sand). Between days
3 – 9, the GAC and sand + GAC biofilters had the lowest effluent DOC concentrations, indicating
higher removal efficiency compared to the sand biofilter. However, from day 9 to day 30 (except
for day 27), effluent DOC concentrations from all biofilters were extremely high, in some cases
exceeding the influent DOC levels. This resulted in low or even negative removal efficiencies,
ranging from –22% to 59%. These trends align with pH observations: from day 2 to 9, the pH of
the GAC and sand + GAC biofilters remained within the ideal range (6.5 – 7.5), supporting
effective microbial activity. After day 9, however, pH levels in all biofilters rose above 9, likely
inhibiting biological processes and contributing to the reduced DOC removal performance.
The reduction in DOC concentrations from influent to effluent was substantial across all
biofilters, aligning with findings from various biological filtration studies. Collins et al. [37]

75

observed 12–33% removal of DOC in different full-scale slow sand filtration (SSF) plants with
EBCTs ranging from 3.8 to 21.9 h. These findings are consistent with the sand biofilter, whose
removal efficiency ranged from 12 – 68% across all DOC concentrations. The DOC removals of
a biological activated carbon filtration, preceded by coagulation/flocculation/clarification and preozonation was reported to be between 12% and 38% [38]. Additionally, full-scale classical
biofiltration treating Grand River water achieved mean TOC removals of 14% with anthracite filter
media and 23% with granular activated carbon filter media [39]. In this study, the GAC biofilter
achieved DOC removal of 12 – 91%. Although the removal efficiency achieved in this study was
significantly higher than that of the previous research, this could be attributed to the pretreatment
methods applied. The diffused aeration oxidized certain organic matter, making them more
amenable to removal in the biofilters. Overall, the average DOC removal efficiency indicates that
sand + GAC biofilter provides the most effective DOC removal across all DOC concentrations.
4.11. Statistical Analyses
4.11.1. Outlier test
Figure 4.1 shows a Grubbs outlier test at the 0.05 level of significance for DOC removal
among the biofiltration systems. In these results, the null hypothesis states that all data values come
from the same normal population. Because the p-value is 1 for DOC removal, which is greater
than the significance level of 0.05, the decision is to fail to reject the null hypothesis because there
is not enough evidence to conclude that an outlier exists. Another Grubbs outlier test at a
significance level of 0.1 was also tested and the decision was to fail to reject the null hypothesis.
Additional Dixon’s Q test at significance levels of 0.05 and 0.1 were also test and the results were
similar to the previous tests. Therefore, there are no outliers at the 5% and 10% level of
significance.

76

Figure 4.1: Outlier plot for DOC removal at α = 0.05 (a) 0.1 (b)
4.11.2. Normality test
Figure 4.1 shows the normality test results at the 0.05 level of significance for all three
biofiltration systems regarding DOC removal efficiency. The mean DOC removal is 43.14%, with
a standard deviation of 27.54%, indicating considerable variability in removal performance. The
Kolmogorov-Smirnov (KS) test was employed due to the sample size exceeding 50 (n = 85),
making it suitable for assessing the normality of large datasets. KS statistic of 0.139 and a p-value
less than 0.010 suggest that the data significantly deviate from a normal distribution, implying that
DOC removal efficiencies are not normally distributed. This deviation may be due to operational
inconsistencies, varying influent water quality, or differences in biofilter media performance.

77

Figure 4.1: Normality test results for the DOC removal efficiency among the biofiltration
systems
4.11.3. Residual plots
An assessment was conducted to verify that the residuals are adequate and meets the necessary
analytical assumptions using the residual plots (Figure 4.1). The residuals versus fits plot proves
that that residuals are randomly distributed about zero, and the variance of residuals is constant
across the range of fitted values. This supports the assumption of homoscedasticity, further proving
the normality test. The residuals versus order plot shows residuals scattered randomly without a
trend, indicating that the residuals are independent from one another. Additionally, normal
probability plot indicate that the residuals are approximately normally distributed because it
follows a straight line. This supports the assumption of normality.

78

Figure 4.1: Residual plots for DOC removal
4.11.4. Correlation test
A Spearman correlation test was preformed to determine the correlation between influent DOC
concentration and the DOC removal efficiency (Table 4.3 and Figure 4.1). This non-parametric
test was selected due to the non-normal distribution of the dataset, as indicated in prior analysis
[159]. In these results, the Spearman correlation between influent DOC concentration and DOC
removal efficiency is -0.798, which indicates that there is a strong negative relationship between
the variables. The confidence interval for rho is from -0.872 to -0.688. The p-value is 0.000, which
indicates that the relationship is statistically significant at the α = 0.05 level. The relationship
between these variables is negative, which indicates that, as DOC concentration increases, DOC
removal decreases, and vice versa.

79

Table 4.3: Pairwise spearman correlations
Sample 1

Sample 2

N

Correlation

95% CI for ρ

P-Value

DOC removal (%)

DOC (mg/L)

85

-0.798

(-0.872, -0.688)

0.000

Figure 4.1: Matrix plot of DOC concentration and DOC removal

4.11.5. Pareto chart
A Pareto chart of the effects was used to determine whether media type or DOC concentration
contributes the most to the variability in the DOC removal (Figure 4.1). The results show that only
DOC concentration is statistically significant at the 0.05 level of significance because the bar
extends past the red line.

80

Figure 4.1: Pareto chart of the standardized effects
4.11.6. Analysis of variance (ANOVA)
An analysis of variance was conducted to determine whether any of the differences between
the means are statistically significant (Table 4.1). A significance level of p ≤ 0.05 was used to
assess the null hypothesis that states that the population means are all equal. The results indicate
that the null hypothesis – stating that the mean DOC removal values are equal across all DOC
concentrations and media types – can be rejected. This conclusion is based on a p-value is less
than the significance level of 0.05, suggesting that at least one group differs significantly in mean
DOC removal. However, the p-value for media type (0.201) exceeds the significance level of 0.05,
indicating that media type is not a statistically significant factor in influencing DOC removal
efficiency. This finding aligns with the trends observed in the Pareto chart, further supporting the
conclusion that while DOC concentration significantly affects removal efficiency, the type of
media used does not have a statistically distinguishable impact under the tested conditions.

81

Table 4.1: Analysis of variance
Source

DF

Adj SS

Adj MS

F-Value

P-Value

Regression

3

41492.2

13830.7

50.47

0.000

DOC (mg/L)

1

40509.0

40509.0

147.83

0.000

Media type

2

897.9

449.0

1.64

0.201

81

22195.3

274.0

Lack-of-fit

5

415.8

83.2

0.29

0.917

Pure Error

76

21779.5

286.6

84

63687.5

Error

Total

4.11.7. Tukey post-hoc test
Based on the ANOVA results, the type of media used did not exhibit a statistically
significant effect under the tested conditions. To further investigate differences among the
biofilters, a Tukey post-hoc test was performed to identify which specific biofilters showed
significant differences in their mean DOC removal performance (Figure 4.15). The test was
conducted using a significance level of p ≤ 0.05, evaluating the null hypothesis that the difference
in means is zero. The results showed that at influent DOC concentrations of 20 mg/L and 50 mg/L,
the null hypothesis could be rejected, indicating statistically significant differences among the
biofilters. In contrast, at 100 mg/L DOC, the null hypothesis could not be rejected, as the p-value
exceeded the 0.05 threshold. These findings suggest that at lower DOC levels (20 and 50 mg/L),
the biofilters were effective in reducing DOC concentrations. However, at the higher influent
concentration of 100 mg/L, DOC levels increased rather than decreased, indicating diminished or
adverse performance under those conditions.

82

Figure 4.1: Interval plots of DOC removal vs media type at DOC = 20 mg/L (a) 50 mg/L (b), and 100 mg/L (c)

83

Chapter 5 Conclusions and Recommendations
5.1. Conclusions
The study investigated the impact of media type and varying DOC concentrations on the
treatment performance of biofiltration. A consistent decline in dissolved oxygen (DO) across the
treatment units confirmed active microbial respiration, a key mechanism in DOC removal. The
system operated within an optimal temperature range (19–21 °C), supporting effective biological
activity. Despite fluctuations in influent turbidity, all biofilters achieved high removal efficiencies,
with the sand + GAC filter producing the clearest effluent.
UVA₅₂₄ measurements showed that organic matter was highest in influent water, with the sand
biofilter demonstrating the most stable reduction. pH levels varied due to the addition of alkaline
compounds, occasionally exceeding the ideal range, particularly at higher DOC concentrations.
Alkalinity fluctuated between 259 and 1470 mg/L, contributing to pH buffering but showing
minimal impact on performance.
ATP analysis revealed that the sand biofilter supported the highest microbial activity over time,
while EPS analysis indicated that TB-EPS was protein-rich and structurally stable, whereas LBEPS was more sugar-dominant and dynamic. These findings highlight the role of EPS in biofilm
development and resilience.
DOC removal efficiency varied with concentration and time. At 20 mg/L, steady state was
reached by day 8, with GAC and sand + GAC filters achieving the highest removal. At 50 and 100
mg/L, performance declined, likely due to reduced microbial activity and elevated pH. ANOVA
confirmed that DOC concentration significantly influenced removal efficiency, while media type
did not. However, Tukey post-hoc test showed that at DOC concentrations of 20 and 50 mg/L,

84

biofilters significantly reduced DOC levels, while at 100 mg/L, no significant reduction was
observed, indicating diminished effectiveness at higher concentrations.
These results emphasize the importance of pretreatment and operational control in optimizing
biofiltration under variable conditions.
5.2. Limitations of the study
This research had some limitations:
-

This study was limited to the use of synthetic wildfire-impacted water in a laboratorycontrolled environment.

-

This study was constrained by the deliberate control of only two operational parameters—
temperature, EBCT and HLR. The exclusion of other potentially influential factors may
limit the generalizability of the findings to systems operating under different conditions.

-

A significant challenge encountered in this study was use of easily biodegradable
chemicals to increase the DOC concentration. The DOC concentrations of the influent
water decrease after a couple of days thereby causing inconsistency in the influent DOC
concentration.

-

Several factors such as fluctuating pollutant loads, biofilm development, and external
environmental changes made it challenging to accurately characterize microbial behavior.

-

The study did not examine biofiltration system performance under colder temperature
conditions, which are known to influence microbial activity and treatment efficiency. This
limitation may reduce the applicability of findings to colder climates.

5.3. Recommendations
Based on the results obtained from this thesis, it is necessary to further investigate the following
research areas:
85

a. Future studies should consider using lake water as a test matrix to better simulate the
complex and variable conditions of wildfire-impacted source water. Unlike synthetic
or controlled laboratory water, lake water contains a diverse mixture of natural organic
matter, particulates, and microbial communities, which more accurately reflect the
challenges faced in real-world treatment scenarios following wildfire events.
b. To enhance the robustness and applicability of future studies, it is recommended that
additional environmental factors–such as pH be systematically controlled and
analyzed. Expanding the scope of controlled environmental parameters will provide a
more comprehensive understanding of their interactions and overall impact on system
performance.
c. To address the inconsistency in influent DOC concentrations caused by the rapid
degradation of easily biodegradable compounds, future studies should consider using a
blend of biodegradable and more recalcitrant organic matter. This approach would help
maintain more stable DOC levels over time, better simulating real-world conditions
and allowing for more consistent evaluation of biofiltration performance.
d. A comprehensive genomic analysis of microbial communities within biofilters can
provide deeper insights into the specific environmental conditions required for optimal
microbial growth and performance. Such understanding is essential for tailoring
biofiltration systems to enhance treatment efficiency.
e. For future optimization studies, it is essential to investigate the performance of
biofiltration systems under colder temperature conditions, as temperature significantly
influences microbial activity and treatment efficiency.

86

References
[1]

L. Lin, H. Yang, and X. Xu, “Effects of Water Pollution on Human Health and Disease
Heterogeneity: A Review,” Front. Environ. Sci., vol. 10, Jun. 2022, doi:
10.3389/fenvs.2022.880246.

[2]

A. Boretti and L. Rosa, “Reassessing the projections of the World Water Development
Report,” Npj Clean Water, vol. 2, no. 1, pp. 1–6, Jul. 2019, doi: 10.1038/s41545-019-00399.

[3]

M. J. Paul, S. D. LeDuc, M. G. Lassiter, L. C. Moorhead, P. D. Noyes, and S. G. Leibowitz,
“Wildfire Induces Changes in Receiving Waters: A Review With Considerations for Water
Quality Management,” Water Resour. Res., vol. 58, no. 9, p. e2021WR030699, 2022, doi:
10.1029/2021WR030699.

[4]

M. D. Sequeira, A. Castilho, A. O. Tavares, and P. Dinis, “The Rural Fires of 2017 and
Their Influences on Water Quality: An Assessment of Causes and Effects,” Int. J. Environ.
Res. Public. Health, vol. 20, no. 1, p. 32, Dec. 2022, doi: 10.3390/ijerph20010032.

[5]

A. J. Rust, J. Randell, A. S. Todd, and T. S. Hogue, “Wildfire impacts on water quality,
macroinvertebrate, and trout populations in the Upper Rio Grande,” For. Ecol. Manag.,
vol. 453, p. 117636, Dec. 2019, doi: 10.1016/j.foreco.2019.117636.

[6]

E. A. J. Blackburn, S. E. Dickson-Anderson, W. B. Anderson, and M. B. Emelko,
“Biological Filtration is Resilient to Wildfire Ash-Associated Organic Carbon Threats to
Drinking Water Treatment,” ACS EST Water, vol. 3, no. 3, pp. 639–649, Mar. 2023, doi:
10.1021/acsestwater.2c00209.

87

[7]

M. Rodriguez and I. Delpla, “Climate Changes And Drinking Water In Sustainable Cities:
Impacts And Adaptation,” WIT Trans. Ecol. Environ., vol. 223, pp. 573–582, 2017, doi:
10.2495/SC170501.

[8]

M. Sander, M. Sander, T. Burbidge, and J. Beecker, “The efficacy and safety of sunscreen
use for the prevention of skin cancer,” CMAJ Can. Med. Assoc. J., vol. 192, no. 50, pp.
E1802–E1808, Dec. 2020, doi: 10.1503/cmaj.201085.

[9]

B. Desye, B. Belete, Z. Asfaw Gebrezgi, and T. Terefe Reda, “Efficiency of Treatment
Plant and Drinking Water Quality Assessment from Source to Household, Gondar City,
Northwest Ethiopia,” J. Environ. Public Health, vol. 2021, p. 9974064, May 2021, doi:
10.1155/2021/9974064.

[10]

A. A. Ali, O. T. Iorhemen, and R. W. Thring, “Climate adaptation and resilience of
biofiltration as a low-cost technological solution for water treatment – A critical review,”
Clean. Water, vol. 3, p. 100062, Jun. 2025, doi: 10.1016/j.clwat.2024.100062.

[11]

O. D. Basu, S. Dhawan, and K. Black, “Applications of biofiltration in drinking water
treatment – a review,” J. Chem. Technol. Biotechnol., vol. 91, no. 3, pp. 585–595, 2016,
doi: 10.1002/jctb.4860.

[12]

P. Lin, Z. Liao, G. Wu, L. Yang, J. Fu, and Y. Luo, “Recent Advances in Biofiltration for
PPCP Removal from Water,” Water, vol. 16, no. 13, Art. no. 13, Jan. 2024, doi:
10.3390/w16131888.

[13]

P. Sinha and S. Mukherji, “Biofiltration Process for Treatment of Water and Wastewater,”
Trans. Indian Natl. Acad. Eng., vol. 7, no. 4, pp. 1069–1091, Dec. 2022, doi:
10.1007/s41403-022-00360-0.

88

[14]

P. Thuptimdang, S. Siripattanakul-Ratpukdi, T. Ratpukdi, A. Youngwilai, and E. Khan,
“Biofiltration for treatment of recent emerging contaminants in water: Current and future
perspectives,” Water Environ. Res., vol. 93, no. 7, pp. 972–992, 2021, doi:
10.1002/wer.1493.

[15]

I. Delpla, A.-V. Jung, E. Baures, M. Clement, and O. Thomas, “Impacts of climate change
on surface water quality in relation to drinking water production,” Environ. Int., vol. 35,
no. 8, pp. 1225–1233, Nov. 2009, doi: 10.1016/j.envint.2009.07.001.

[16]

A. M. Michalak, “Study role of climate change in extreme threats to water quality,” Nature,
vol. 535, no. 7612, Art. no. 7612, Jul. 2016, doi: 10.1038/535349a.

[17]

O. Thorne and R. A. Fenner, “The impact of climate change on reservoir water quality and
water treatment plant operations: a UK case study,” Water Environ. J., vol. 25, no. 1, pp.
74–87, 2011, doi: 10.1111/j.1747-6593.2009.00194.x.

[18]

M. T. H. van Vliet and J. J. G. Zwolsman, “Impact of summer droughts on the water quality
of the Meuse river,” J. Hydrol., vol. 353, no. 1, pp. 1–17, May 2008, doi:
10.1016/j.jhydrol.2008.01.001.

[19]

L. Prathumratana, S. Sthiannopkao, and K. W. Kim, “The relationship of climatic and
hydrological parameters to surface water quality in the lower Mekong River,” Environ.
Int., vol. 34, no. 6, pp. 860–866, Aug. 2008, doi: 10.1016/j.envint.2007.10.011.

[20]

L. M. Mosley, “Drought impacts on the water quality of freshwater systems; review and
integration,”

Earth-Sci.

Rev.,

vol.

140,

pp.

203–214,

Jan.

2015,

doi:

10.1016/j.earscirev.2014.11.010.
[21]

E. Gaitán, R. Monjo, J. Pórtoles, and M. R. Pino-Otín, “Projection of temperatures and heat
and cold waves for Aragón (Spain) using a two-step statistical downscaling of CMIP5

89

model outputs,” Sci. Total Environ., vol. 650, pp. 2778–2795, Feb. 2019, doi:
10.1016/j.scitotenv.2018.09.352.
[22]

G. Gómez-Martínez et al., “Effects of Climate Change on Water Quality in the Jucar River
Basin (Spain),” Water, vol. 13, no. 17, Art. no. 17, Jan. 2021, doi: 10.3390/w13172424.

[23]

M. Zhu et al., “The role of tropical cyclones in stimulating cyanobacterial (Microcystis
spp.) blooms in hypertrophic Lake Taihu, China,” Harmful Algae, vol. 39, pp. 310–321,
Oct. 2014, doi: 10.1016/j.hal.2014.09.003.

[24]

H. W. Paerl et al., “Ecosystem impacts of three sequential hurricanes (Dennis, Floyd, and
Irene) on the United States’ largest lagoonal estuary, Pamlico Sound, NC,” Proc. Natl.
Acad. Sci., vol. 98, no. 10, pp. 5655–5660, May 2001, doi: 10.1073/pnas.101097398.

[25]

B. Pinel-Alloul, E. Prepas, D. Planas, R. Steedman, and T. Charette, “Watershed Impacts
of Logging and Wildfire: Case Studies in Canada,” Lake Reserv. Manag., vol. 18, no. 4,
pp. 307–318, Dec. 2002, doi: 10.1080/07438140209353937.

[26]

M. B. Emelko, U. Silins, K. D. Bladon, and M. Stone, “Implications of land disturbance
on drinking water treatability in a changing climate: Demonstrating the need for ‘source
water supply and protection’ strategies,” Water Res., vol. 45, no. 2, pp. 461–472, Jan. 2011,
doi: 10.1016/j.watres.2010.08.051.

[27]

A. K. Hohner, K. Cawley, J. Oropeza, R. S. Summers, and F. L. Rosario-Ortiz, “Drinking
water treatment response following a Colorado wildfire,” Water Res., vol. 105, pp. 187–
198, Nov. 2016, doi: 10.1016/j.watres.2016.08.034.

[28]

F. Giorgi and P. Lionello, “Climate change projections for the Mediterranean region,”
Glob.

Planet.

Change,

vol.

63,

no.

10.1016/j.gloplacha.2007.09.005.

90

2,

pp.

90–104,

Sep.

2008,

doi:

[29]

J. Cos, F. Doblas-Reyes, M. Jury, R. Marcos, P.-A. Bretonnière, and M. Samsó, “The
Mediterranean climate change hotspot in the CMIP5 and CMIP6 projections,” Earth Syst.
Dyn., vol. 13, no. 1, pp. 321–340, Feb. 2022, doi: 10.5194/esd-13-321-2022.

[30]

M. Seker and V. Gumus, “Projection of temperature and precipitation in the Mediterranean
region through multi-model ensemble from CMIP6,” Atmospheric Res., vol. 280, p.
106440, Dec. 2022, doi: 10.1016/j.atmosres.2022.106440.

[31]

Z. Li, R. M. Clark, S. G. Buchberger, and Y. Jeffrey Yang, “Evaluation of Climate Change
Impact on Drinking Water Treatment Plant Operation,” J. Environ. Eng., vol. 140, no. 9,
p. A4014005, Sep. 2014, doi: 10.1061/(ASCE)EE.1943-7870.0000824.

[32]

R. G. Skaland et al., “Impacts of climate change on drinking water quality in Norway,” J.
Water Health, vol. 20, no. 3, pp. 539–550, Feb. 2022, doi: 10.2166/wh.2022.264.

[33]

D. Wang, “Raw water quality assessment for the treatment of drinking water,” Environ.
Earth Sci., vol. 75, no. 19, p. 1327, Oct. 2016, doi: 10.1007/s12665-016-6134-z.

[34]

M. Kyei, E. Appiah-Effah, and K. Akodwaa-Boadi, “Mechanistic interaction between
climate variables rainfall and temperature on surface water quality and water treatment
costs at the Barekese Headworks, Ghana: A time series analysis and water quality index
modelling

approach,”

Sci.

Afr.,

vol.

22,

p.

e01953,

Nov.

2023,

doi:

10.1016/j.sciaf.2023.e01953.
[35]

Z. Zhou, T. Huang, W. Ma, Y. Li, and K. Zeng, “Impacts of water quality variation and
rainfall runoff on Jinpen Reservoir, in Northwest China,” Water Sci. Eng., vol. 8, no. 4, pp.
301–308, Oct. 2015, doi: 10.1016/j.wse.2015.12.003.

91

[36]

R. L. Crouch, H. J. Timmenga, T. R. Barber, and P. C. Fuchsman, “Post-fire surface water
quality: Comparison of fire retardant versus wildfire-related effects,” Chemosphere, vol.
62, no. 6, pp. 874–889, Feb. 2006, doi: 10.1016/j.chemosphere.2005.05.031.

[37]

C. A. Emmerton et al., “Severe western Canadian wildfire affects water quality even at
large

basin

scales,”

Water

Res.,

vol.

183,

p.

116071,

Sep.

2020,

doi:

10.1016/j.watres.2020.116071.
[38]

E. Towler, B. Raucher, B. Rajagopalan, A. Rodriguez, D. Yates, and R. S. Summers,
“Incorporating Climate Uncertainty in a Cost Assessment for New Municipal Source
Water,” J. Water Resour. Plan. Manag., vol. 138, no. 5, pp. 396–402, Sep. 2012, doi:
10.1061/(ASCE)WR.1943-5452.0000150.

[39]

M. Valdivia-Garcia, P. Weir, D. W. Graham, and D. Werner, “Predicted Impact of Climate
Change on Trihalomethanes Formation in Drinking Water Treatment,” Sci. Rep., vol. 9,
no. 1, Art. no. 1, Jul. 2019, doi: 10.1038/s41598-019-46238-0.

[40]

P. Mote, A. Petersen, S. Reeder, H. Shipman, and L. Whitely Binder, “Sea Level Rise in
the Coastal Waters of Washington State,” 2008, Accessed: Sep. 24, 2023. [Online].
Available: https://digital.lib.washington.edu:443/researchworks/handle/1773/34589

[41]

C. H. S. Williams et al., “Net precipitation in burned and unburned subalpine forest stands
after wildfire in the northern Rocky Mountains,” Int. J. Wildland Fire, vol. 28, no. 10, pp.
750–760, Sep. 2019, doi: 10.1071/WF18181.

[42]

J. Abraham, K. Dowling, and S. Florentine, “Risk of post-fire metal mobilization into
surface water resources: A review,” Sci. Total Environ., vol. 599–600, pp. 1740–1755, Dec.
2017, doi: 10.1016/j.scitotenv.2017.05.096.

92

[43]

J. I. Price, S. Renzetti, D. Dupont, W. Adamowicz, and M. B. Emelko, “Production Costs,
Inefficiency, and Source Water Quality: A Stochastic Cost Frontier Analysis of Canadian
Water Utilities,” Land Econ., vol. 93, no. 1, pp. 1–11, 2017.

[44]

D. C. Major, A. Omojola, M. Dettinger, R. T. Hanson, and R. Sanchez-Rodriguez,
“Climate change, water, and wastewater in cities,” in Climate Change and Cities: First
Assessment Report of the Urban Climate Change Research Network, C. Rosenzweig, S.
Mehrotra, S. A. Hammer, and W. D. Solecki, Eds., Cambridge: Cambridge University
Press, 2011, pp. 113–144. doi: 10.1017/CBO9780511783142.011.

[45]

Intergovernmental Panel on Climate Change (IPCC), Climate Change 2014 – Impacts,
Adaptation and Vulnerability: Part B: Regional Aspects: Working Group II Contribution
to the IPCC Fifth Assessment Report: Volume 2: Regional Aspects, vol. 2. Cambridge:
Cambridge University Press, 2014. doi: 10.1017/CBO9781107415386.

[46]

A. Zouboulis and A. Tolkou, “Effect of Climate Change in Wastewater Treatment Plants:
Reviewing the Problems and Solutions,” in Managing Water Resources under Climate
Uncertainty: Examples from Asia, Europe, Latin America, and Australia, S. Shrestha, A.
K. Anal, P. A. Salam, and M. van der Valk, Eds., in Springer Water. , Cham: Springer
International Publishing, 2015, pp. 197–220. doi: 10.1007/978-3-319-10467-6_10.

[47]

M. von Sperling and C. A. de L. Cher, Biological Wastewater Treatment in Warm Climate
Regions, vol. I, 3 vols. IWA Publishing, 2005. Accessed: Sep. 24, 2023. [Online].
Available:

https://www.iwapublishing.com/books/9781843391074/biological-

wastewater-treatment-warm-climate-regions-volume-ii
[48]

M. J. Brandt, K. M. Johnson, A. J. Elphinston, and D. D. Ratnayaka, “Chapter 7 Chemistry, Microbiology and Biology of Water,” in Twort’s Water Supply (Seventh

93

Edition), M. J. Brandt, K. M. Johnson, A. J. Elphinston, and D. D. Ratnayaka, Eds., Boston:
Butterworth-Heinemann, 2017, pp. 235–321. doi: 10.1016/B978-0-08-100025-0.00007-7.
[49]

L. O. Cossu, S. F. De Aquino, C. R. Mota Filho, C. J. Smith, and M. Vignola, “Review on
Pesticide Contamination and Drinking Water Treatment in Brazil: The Need for Improved
Treatment Methods,” ACS EST Water, vol. 4, no. 9, pp. 3629–3644, Sep. 2024, doi:
10.1021/acsestwater.4c00063.

[50]

M. C. Razali, N. A. Wahab, N. Sunar, and N. H. Shamsudin, “Existing Filtration Treatment
on Drinking Water Process and Concerns Issues,” Membranes, vol. 13, no. 3, Art. no. 3,
Mar. 2023, doi: 10.3390/membranes13030285.

[51]

E. S. Rigobello, A. D. B. Dantas, L. Di Bernardo, and E. M. Vieira, “Removal of diclofenac
by conventional drinking water treatment processes and granular activated carbon
filtration,”

Chemosphere,

vol.

92,

no.

2,

pp.

184–191,

Jun.

2013,

doi:

10.1016/j.chemosphere.2013.03.010.
[52]

J. Benner et al., “Is biological treatment a viable alternative for micropollutant removal in
drinking water treatment processes?,” Water Res., vol. 47, no. 16, pp. 5955–5976, Oct.
2013, doi: 10.1016/j.watres.2013.07.015.

[53]

L. P. Sabogal-Paz, L. C. Campos, A. Bogush, and M. Canales, “Household slow sand filters
in intermittent and continuous flows to treat water containing low mineral ion
concentrations and Bisphenol A,” Sci. Total Environ., vol. 702, p. 135078, Feb. 2020, doi:
10.1016/j.scitotenv.2019.135078.

[54]

T. Kekes, C. Tzia, and G. Kolliopoulos, “Drinking and Natural Mineral Water: Treatment
and Quality–Safety Assurance,” Water, vol. 15, no. 13, Art. no. 13, Jan. 2023, doi:
10.3390/w15132325.

94

[55]

C. Teodosiu, A.-F. Gilca, G. Barjoveanu, and S. Fiore, “Emerging pollutants removal
through advanced drinking water treatment: A review on processes and environmental
performances assessment,” J. Clean. Prod., vol. 197, pp. 1210–1221, Oct. 2018, doi:
10.1016/j.jclepro.2018.06.247.

[56]

N. Zaitseva, V. Zaitsev, and A. Walcarius, “Chromium(VI) removal via reduction–sorption
on bi-functional silica adsorbents,” J. Hazard. Mater., vol. 250–251, pp. 454–461, Apr.
2013, doi: 10.1016/j.jhazmat.2013.02.019.

[57]

O. M. Rodriguez-Narvaez, J. M. Peralta-Hernandez, A. Goonetilleke, and E. R. Bandala,
“Treatment technologies for emerging contaminants in water: A review,” Chem. Eng. J.,
vol. 323, pp. 361–380, Sep. 2017, doi: 10.1016/j.cej.2017.04.106.

[58]

D. P. Grover, J. L. Zhou, P. E. Frickers, and J. W. Readman, “Improved removal of
estrogenic and pharmaceutical compounds in sewage effluent by full scale granular
activated carbon: Impact on receiving river water,” J. Hazard. Mater., vol. 185, no. 2, pp.
1005–1011, Jan. 2011, doi: 10.1016/j.jhazmat.2010.10.005.

[59]

S. A. C. Carabineiro, T. Thavorn-Amornsri, M. F. R. Pereira, and J. L. Figueiredo,
“Adsorption of ciprofloxacin on surface-modified carbon materials,” Water Res., vol. 45,
no. 15, pp. 4583–4591, Oct. 2011, doi: 10.1016/j.watres.2011.06.008.

[60]

Ahuja, S., Larsen, M.C., Eimers, J.L., Patterson, C.L., Sengupta, S., and Schnoor, J.L..,
Comprehensive Water Quality and Purification. Elsevier, 2013.

[61]

S. Snyder and K. Linton, “Removal of EDCs and Pharmaceuticals in Drinking and Reuse
Treatment Processes,” The Water Research Foundation, 2758, Jun. 2007. Accessed: Dec.
01, 2024. [Online]. Available: https://www.waterrf.org/research/projects/removal-edcsand-pharmaceuticals-drinking-and-reuse-treatment-processes

95

[62]

H. Abu Hasan, M. H. Muhammad, and N. ’Izzati Ismail, “A review of biological drinking
water treatment technologies for contaminants removal from polluted water resources,” J.
Water Process Eng., vol. 33, p. 101035, Feb. 2020, doi: 10.1016/j.jwpe.2019.101035.

[63]

M. Ravnjak, J. Vrtovšek, and A. Pintar, “Denitrification of drinking water in a two-stage
membrane bioreactor by using immobilized biomass,” Bioresour. Technol., vol. 128, pp.
804–808, Jan. 2013, doi: 10.1016/j.biortech.2012.10.055.

[64]

X. Li and H. P. Chu, “Membrane bioreactor for the drinking water treatment of polluted
surface water supplies,” Water Res., vol. 37, no. 19, pp. 4781–4791, Nov. 2003, doi:
10.1016/S0043-1354(03)00424-X.

[65]

S. Rodriguez-Mozaz et al., “Pharmaceuticals and pesticides in reclaimed water: Efficiency
assessment of a microfiltration–reverse osmosis (MF–RO) pilot plant,” J. Hazard. Mater.,
vol. 282, pp. 165–173, Jan. 2015, doi: 10.1016/j.jhazmat.2014.09.015.

[66]

A. K. Venkatesan, C.-S. Lee, and C. J. Gobler, “Hydroxyl-radical based advanced
oxidation processes can increase perfluoroalkyl substances beyond drinking water
standards: Results from a pilot study,” Sci. Total Environ., vol. 847, p. 157577, Nov. 2022,
doi: 10.1016/j.scitotenv.2022.157577.

[67]

A. Giwa et al., “Recent advances in advanced oxidation processes for removal of
contaminants from water: A comprehensive review,” Process Saf. Environ. Prot., vol. 146,
pp. 220–256, Feb. 2021, doi: 10.1016/j.psep.2020.08.015.

[68]

X. Bai, I. J. T. Dinkla, and G. Muyzer, “Microbial ecology of biofiltration used for
producing safe drinking water,” Appl. Microbiol. Biotechnol., vol. 106, no. 13, pp. 4813–
4829, Aug. 2022, doi: 10.1007/s00253-022-12013-x.

96

[69]

L. G. Terry and R. S. Summers, “Biodegradable organic matter and rapid-rate biofilter
performance: A review,” Water Res., vol. 128, pp. 234–245, Jan. 2018, doi:
10.1016/j.watres.2017.09.048.

[70]

D. S. Chaudhary, S. Vigneswaran, H.-H. Ngo, W. G. Shim, and H. Moon, “Biofilter in
Water and Wastewater Treatment,” Korean J. Chem. Eng., vol. 20, no. 6, pp. 1054–1065,
2003.

[71]

R. Pachaiappan, L. Cornejo-Ponce, R. Rajendran, K. Manavalan, V. Femilaa Rajan, and F.
Awad, “A review on biofiltration techniques: recent advancements in the removal of
volatile organic compounds and heavy metals in the treatment of polluted water,”
Bioengineered,

vol.

13,

no.

4,

pp.

8432–8477,

2022,

doi:

10.1080/21655979.2022.2050538.
[72]

B. Jess et al., “Biofiltration Guidance Manual for Drinking Water Facilities,” The Water
Research Foundation, 4719, Dec. 2020. Accessed: Jun. 11, 2024. [Online]. Available:
https://www.waterrf.org/resource/biofiltration-guidance-manual-drinking-water-facilities

[73]

A. N. Evans, J. T. Carter, J. Brown, and C. Lauderdale, “North American biofiltration
practices,” AWWA Water Sci., vol. 1, no. 4, p. e1152, 2019, doi: 10.1002/aws2.1152.

[74]

E. Dickenson et al., “Simultaneous Removal of Multiple Chemical Contaminants Using
Biofiltration,” Water Research Foundation, Denver, Colorado, 4559, 2018. Accessed: Jun.
18, 2024. [Online]. Available: https://www.waterrf.org/research/projects/simultaneousremoval-multiple-chemical-contaminants-using-biofiltration

[75]

Z. Du, R. Jia, C. Li, P. Cui, W. Song, and J. Liu, “Pilot-scale UV/H2O2-BAC process for
drinking water treatment – Analysis and comparison of different activated carbon
columns,” Chem. Eng. J., vol. 382, p. 123044, Feb. 2020, doi: 10.1016/j.cej.2019.123044.

97

[76]

T. C. Guarin and K. R. Pagilla, “Microbial community in biofilters for water reuse
applications: A critical review,” Sci. Total Environ., vol. 773, p. 145655, Jun. 2021, doi:
10.1016/j.scitotenv.2021.145655.

[77]

X. Liu et al., “Investigation of extracellular polymeric substances (EPS) in four types of
sludge: Factors influencing EPS properties and sludge granulation,” J. Water Process Eng.,
vol. 40, p. 101924, Apr. 2021, doi: 10.1016/j.jwpe.2021.101924.

[78]

D. Ke, R. Li, and C. Liu, “The feedback interaction between biomass accumulation and
heterogeneous flow in porous media: Effect of shear stresses,” J. Hydrol., vol. 597, p.
126083, Jun. 2021, doi: 10.1016/j.jhydrol.2021.126083.

[79]

X. Liu, P. M. Huck, and R. M. Slawson, “Factors Affecting Drinking Water Biofiltration,”
J. AWWA, vol. 93, no. 12, pp. 90–101, 2001, doi: 10.1002/j.1551-8833.2001.tb09358.x.

[80]

J. Wang, D. de Ridder, A. van der Wal, and N. B. Sutton, “Harnessing biodegradation
potential of rapid sand filtration for organic micropollutant removal from drinking water:
A review,” Crit. Rev. Environ. Sci. Technol., vol. 51, no. 18, pp. 2086–2118, Sep. 2021,
doi: 10.1080/10643389.2020.1771888.

[81]

F. Hammes, S. Velten, T. Egli, and T. Juhna, “Biotreatment of Drinking Water,” in
Comprehensive Biotechnology (Second Edition), M. Moo-Young, Ed., Burlington:
Academic Press, 2011, pp. 517–530. doi: 10.1016/B978-0-08-088504-9.00386-X.

[82]

D. M. Golea et al., “Influence of granular activated carbon media properties on natural
organic matter and disinfection by-product precursor removal from drinking water,” Water
Res., vol. 174, p. 115613, May 2020, doi: 10.1016/j.watres.2020.115613.

98

[83]

T. Lin, W. Chen, and L. Wang, “Particle properties in granular activated carbon filter
during drinking water treatment,” J. Environ. Sci., vol. 22, no. 5, pp. 681–688, Jan. 2010,
doi: 10.1016/S1001-0742(09)60163-7.

[84]

L. Rolland, P. Molle, A. Liénard, F. Bouteldja, and A. Grasmick, “Influence of the physical
and mechanical characteristics of sands on the hydraulic and biological behaviors of sand
filters,”

Desalination,

vol.

248,

no.

1,

pp.

998–1007,

Nov.

2009,

doi:

10.1016/j.desal.2008.10.016.
[85]

Z. N. Abudi, “The effect of sand filter characteristics on removal efficiency of organic
matter from grey water,” Al-Qadisiya J. Eng. Sci., vol. 4, no. 2, pp. 143–155, 2011.

[86]

N. A. Akbar, H. A. Aziz, and M. N. Adlan, “The Characteristics of Limestone and
Anthracite Coal as Filter Media in Treating Pollutants from Groundwater,” Int. J. Environ.
Sci. Dev., vol. 12, no. 2, pp. 58–62, Feb. 2021, doi: 10.18178/ijesd.2021.12.2.1318.

[87]

L. Xu, L. C. Campos, J. Li, K. Karu, and L. Ciric, “Removal of antibiotics in sand, GAC,
GAC sandwich and anthracite/sand biofiltration systems,” Chemosphere, vol. 275, p.
130004, Jul. 2021, doi: 10.1016/j.chemosphere.2021.130004.

[88]

C. C. Chien, C. M. Kao, C. W. Chen, C. D. Dong, and C. Y. Wu, “Application of
biofiltration system on AOC removal: Column and field studies,” Chemosphere, vol. 71,
no. 9, pp. 1786–1793, Apr. 2008, doi: 10.1016/j.chemosphere.2007.12.005.

[89]

G. S. Wang, C. H. Liao, H. W. Chen, and H. C. Yang, “Characteristics of natural organic
matter degradation in water by UV/H2O2 treatment,” Environ. Technol., vol. 27, no. 3, pp.
277–287, Mar. 2006, doi: 10.1080/09593332708618638.

99

[90]

M. Kumar et al., “Performance of a biofilter with compost and activated carbon based
packing material for gas-phase toluene removal under extremely high loading rates,”
Bioresour. Technol., vol. 285, p. 121317, Aug. 2019, doi: 10.1016/j.biortech.2019.121317.

[91]

Peta Thiel, L. Zappia, P. Franzmann, B. Warton, M. Alessandrino, and A. Heitz, “Activated
carbon vs anthracite as primary dual media filters – a pilot plant study.,” presented at the
69th Annual Water Industry Engineers and Operators Conference, Bendigo, 2006, pp. 8–
14.

[Online].

Available:

chrome-

extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.wioa.org.au/conference_pap
ers/06_vic/documents/PetaThiel1.pdf
[92]

M. Emelko, P. Huck, B. Coffey, and E. SMITH, “Effects of Media, Backwash, and
Temperature on Full-Scale Biological Filtration,” J. Am. Water Works Assoc. - J AMER
WATER WORK ASSN, vol. 98, pp. 61–73, Dec. 2006, doi: 10.1002/j.15518833.2006.tb07824.x.

[93]

D. M. Moll, R. S. Summers, A. C. Fonseca, and W. Matheis, “Impact of Temperature on
Drinking Water Biofilter Performance and Microbial Community Structure,” Environ. Sci.
Technol., vol. 33, no. 14, pp. 2377–2382, Jul. 1999, doi: 10.1021/es9900757.

[94]

V. W. Hoyland, W. R. Knocke, J. O. Falkinham, A. Pruden, and G. Singh, “Effect of
drinking water treatment process parameters on biological removal of manganese from
surface

water,”

Water

Res.,

vol.

66,

pp.

31–39,

Dec.

2014,

doi:

10.1016/j.watres.2014.08.006.
[95]

P. J. Evans, J. L. Smith, M. W. LeChevallier, O. D. Schneider, L. A. Weinrich, and P. K.
Jjemba, “A Monitoring and Control Toolbox for Biological Filtration,” Water Research
Foundation, Denver, Colorado, 4231, 2013. Accessed: Jul. 01, 2024. [Online]. Available:

100

https://www.waterrf.org/research/projects/monitoring-and-control-toolbox-biologicalfiltration
[96]

J. Hooper, K. Vickstrom, P. Evans, C. Alito, S. Black, and C. Lauderdale, “Guidance
Manual for Monitoring Biological Filtration of Drinking Water,” Water Research
Foundation, Denver, Colorado, 4620, 2019. Accessed: Jul. 01, 2024. [Online]. Available:
https://www.waterrf.org/research/projects/guidance-manual-monitoring-biologicalfiltration-drinking-water

[97]

M. Vines and L. G. Terry, “Evaluation of the biodegradability of fluorescent dissolved
organic matter via biological filtration,” AWWA Water Sci., vol. 2, no. 5, p. e1201, 2020,
doi: 10.1002/aws2.1201.

[98]

Z. Liu, K. M. Lompe, M. Mohseni, P. R. Bérubé, S. Sauvé, and B. Barbeau, “Biological
ion exchange as an alternative to biological activated carbon for drinking water treatment,”
Water Res., vol. 168, p. 115148, Jan. 2020, doi: 10.1016/j.watres.2019.115148.

[99]

N. Moona, A. Holmes, U. J. Wünsch, T. J. R. Pettersson, and K. R. Murphy, “Full-Scale
Manipulation of the Empty Bed Contact Time to Optimize Dissolved Organic Matter
Removal by Drinking Water Biofilters,” ACS EST Water, vol. 1, no. 5, pp. 1117–1126,
May 2021, doi: 10.1021/acsestwater.0c00105.

[100] Y.-S. Ko, Y.-J. Lee, and S. Nam, “Evaluation of a pilot scale dual media biological
activated carbon process for drinking water,” Korean J. Chem. Eng., vol. 24, no. 2, pp.
253–260, Mar. 2007, doi: 10.1007/s11814-007-5038-8.
[101] J. Brown, G. Upadhyaya, J. Carter, T. Brown, and C. Lauderdale, “North American
Biofiltration Knowledge Base,” Water Research Foundation, Denver, Colorado, 4459,

101

2016.

Accessed:

Jul.

01,

2024.

[Online].

Available:

https://www.waterrf.org/research/projects/north-american-biofiltration-knowledge-base
[102] S. Jeong et al., “Effect of engineered environment on microbial community structure in
biofilter and biofilm on reverse osmosis membrane,” Water Res., vol. 124, pp. 227–237,
Nov. 2017, doi: 10.1016/j.watres.2017.07.064.
[103] H. C. Granger, A. K. Stoddart, and G. A. Gagnon, “Direct Biofiltration for Manganese
Removal from Surface Water,” J. Environ. Eng., vol. 140, no. 4, p. 04014006, Apr. 2014,
doi: 10.1061/(ASCE)EE.1943-7870.0000819.
[104] L. Bai et al., “Varying removal of emerging organic contaminants in drinking water
treatment residue-based biofilter systems: Influence of hydraulic loading rates on
microbiology,”

Chem.

Eng.

J.,

vol.

491,

p.

152169,

Jul.

2024,

doi:

10.1016/j.cej.2024.152169.
[105] X.-X. Zhang et al., “Influences of hydraulic loading rate on SVOC removal and microbial
community structure in drinking water treatment biofilters,” J. Hazard. Mater., vol. 178,
no. 1, pp. 652–657, Jun. 2010, doi: 10.1016/j.jhazmat.2010.01.135.
[106] M. Turan, “Backwashing of granular media filters and membranes for water treatment: a
review,” AQUA - Water Infrastruct. Ecosyst. Soc., vol. 72, no. 3, pp. 274–298, Feb. 2023,
doi: 10.2166/aqua.2023.207.
[107] T. G. Kim, J. Yun, S.-H. Hong, and K.-S. Cho, “Effects of water temperature and
backwashing on bacterial population and community in a biological activated carbon
process at a water treatment plant,” Appl. Microbiol. Biotechnol., vol. 98, no. 3, pp. 1417–
1427, Feb. 2014, doi: 10.1007/s00253-013-5057-9.

102

[108] W. Qi, W. Li, J. Zhang, X. Wu, J. Zhang, and W. Zhang, “Effect of biological activated
carbon filter depth and backwashing process on transformation of biofilm community,”
Front. Environ. Sci. Eng., vol. 13, no. 1, p. 15, Dec. 2018, doi: 10.1007/s11783-019-11000.
[109] X. Liao, C. Chen, J. Zhang, Y. Dai, X. Zhang, and S. Xie, “Operational performance,
biomass and microbial community structure: impacts of backwashing on drinking water
biofilter,” Environ. Sci. Pollut. Res., vol. 22, no. 1, pp. 546–554, Jan. 2015, doi:
10.1007/s11356-014-3393-7.
[110] S. M. Korotta-Gamage, A. Sathasivan, and K. C. Bal Krishna, “Effect of backwashing
biologically activated carbon on coagulability of organics in surface water,” Sci. Total
Environ., vol. 785, p. 147165, Sep. 2021, doi: 10.1016/j.scitotenv.2021.147165.
[111] S. Ikhlef and O. D. Basu, “Influence of backwash regime on biofilter performance in
drinking water treatment,” J. Chem. Technol. Biotechnol., vol. 92, no. 7, pp. 1777–1784,
2017, doi: 10.1002/jctb.5178.
[112] S. You-qun, “Experimental Research on Removal of Organic Pollutants of Drinking Water
by Biofiltration,” J. Hunan City Univ., 2007, Accessed: Jun. 30, 2024. [Online]. Available:
https://www.semanticscholar.org/paper/Experimental-Research-on-Removal-of-Organicof-by-You-qun/e542ff5c13ef241e4733ffe05c9625f16e4f34df
[113] Z. Lu et al., “Can we shape microbial communities to enhance biological activated carbon
filter

performance?,”

Water

Res.,

vol.

10.1016/j.watres.2022.118104.

103

212,

p.

118104,

Apr.

2022,

doi:

[114] T. Ya et al., “Successional Dynamics of Molecular Ecological Network of Anammox
Microbial Communities under Elevated Salinity,” Water Res., vol. 188, p. 116540, Jan.
2021, doi: 10.1016/j.watres.2020.116540.
[115] S. Banerjee, K. Schlaeppi, and M. G. A. van der Heijden, “Keystone taxa as drivers of
microbiome structure and functioning,” Nat. Rev. Microbiol., vol. 16, no. 9, pp. 567–576,
Sep. 2018, doi: 10.1038/s41579-018-0024-1.
[116] A. Palomo, A. Dechesne, B. F. Smets, and Y. Zheng, “Narrow host range phages infect
essential bacteria for water purification reactions in groundwater-fed rapid sand filters,”
Water Res., vol. 245, p. 120655, Oct. 2023, doi: 10.1016/j.watres.2023.120655.
[117] T. Johnson, J. Butcher, S. Santell, S. Schwartz, S. Julius, and S. LeDuc, “A review of
climate change effects on practices for mitigating water quality impacts,” J. Water Clim.
Change, vol. 13, no. 4, pp. 1684–1705, Mar. 2022, doi: 10.2166/wcc.2022.363.
[118] S. Huber, A. Balz, M. Abert, and W. Pronk, “Characterisation of aquatic humic and nonhumic matter with size-exclusion chromatography - organic carbon detection - organic
nitrogen detection (LC-OCD-OND),” Water Res., vol. 45, pp. 879–85, Sep. 2010, doi:
10.1016/j.watres.2010.09.023.
[119] J. Fu et al., “Pilot investigation of two-stage biofiltration for removal of natural organic
matter in drinking water treatment,” Chemosphere, vol. 166, pp. 311–322, Jan. 2017, doi:
10.1016/j.chemosphere.2016.09.101.
[120] J. Fu, W.-N. Lee, C. Coleman, K. Nowack, J. Carter, and C.-H. Huang, “Removal of
pharmaceuticals and personal care products by two-stage biofiltration for drinking water
treatment,”

Sci.

Total

Environ.,

vol.

10.1016/j.scitotenv.2019.02.026.

104

664,

pp.

240–248,

May

2019,

doi:

[121] C. R. Proctor and F. Hammes, “Drinking water microbiology—from measurement to
management,” Curr. Opin. Biotechnol., vol. 33, pp. 87–94, Jun. 2015, doi:
10.1016/j.copbio.2014.12.014.
[122] A. C. Twort, D. D. Ratnayaka, and M. J. Brandt, Eds., “6 - Chemistry, microbiology and
biology of water,” in Water Supply (Fifth Edition), London: Butterworth-Heinemann,
2000, pp. 196–XI. doi: 10.1016/B978-034072018-9/50008-4.
[123] J. Z. Wang, R. S. Summers, and R. J. Miltner, “Biofiltration performance: part 1,
relationship to biomass,” J. AWWA, vol. 87, no. 12, pp. 55–63, 1995, doi: 10.1002/j.15518833.1995.tb06465.x.
[124] T. L. Zearley and R. S. Summers, “Removal of Trace Organic Micropollutants by Drinking
Water Biological Filters,” Environ. Sci. Technol., vol. 46, no. 17, pp. 9412–9419, Sep.
2012, doi: 10.1021/es301428e.
[125] J. Vandermaesen, B. Horemans, J. Degryse, J. Boonen, E. Walravens, and D. Springael,
“Mineralization of the Common Groundwater Pollutant 2,6-Dichlorobenzamide (BAM)
and its Metabolite 2,6-Dichlorobenzoic Acid (2,6-DCBA) in Sand Filter Units of Drinking
Water Treatment Plants,” Environ. Sci. Technol., vol. 50, no. 18, pp. 10114–10122, Sep.
2016, doi: 10.1021/acs.est.6b01352.
[126] H. G. Hwang, M. S. Kim, S. M. Shin, and C. W. Hwang, “Risk Assessment of the
Schmutzdecke of Biosand Filters: Identification of an Opportunistic Pathogen in
Schmutzdecke Developed by an Unsafe Water Source,” Int. J. Environ. Res. Public.
Health, vol. 11, no. 2, Art. no. 2, Feb. 2014, doi: 10.3390/ijerph110202033.
[127] A. Bharath and R. Turner, “Impact of climate change on skin cancer,” J. R. Soc. Med., vol.
102, no. 6, pp. 215–218, Jun. 2009, doi: 10.1258/jrsm.2009.080261.

105

[128] H.-T. Tran et al., “Advanced Treatment Technologies for the Removal of Organic
Chemical Sunscreens from Wastewater: a Review,” Curr. Pollut. Rep., vol. 8, no. 3, pp.
288–302, Sep. 2022, doi: 10.1007/s40726-022-00221-y.
[129] T. P. Kumar, Rahul, M. A. Kumar, and B. Chandrajit, “Biofiltration of Volatile Organic
Compounds (VOCs) – An Overview,” Res. J. Chem. Sci., vol. 1, no. 8, pp. 83–92, Nov.
2011.
[130] H. Khdhiri, O. Potier, and J.-P. Leclerc, “Aeration efficiency over stepped cascades: Better
predictions from flow regimes,” Water Res., vol. 55, pp. 194–202, May 2014, doi:
10.1016/j.watres.2014.02.022.
[131] APHA, AWWA, and WEF, Standard Methods for the Examination of Water and
Wastewater, 24th ed. Washington DC: APHA Press, 2023. Accessed: Jan. 21, 2025.
[Online]. Available: https://www.standardmethods.org/
[132] Health Canada, “Guidance on natural organic matter in drinking water.” Water and Air
Quality Bureau, Healthy Environments and Consumer Safety Branch, 2020.
[133] S. Felz, S. Al-Zuhairy, O. A. Aarstad, M. C. M. van Loosdrecht, and Y. M. Lin, “Extraction
of Structural Extracellular Polymeric Substances from Aerobic Granular Sludge,” J. Vis.
Exp. JoVE, no. 115, p. 54534, Sep. 2016, doi: 10.3791/54534.
[134] H. Liu and H. H. P. Fang, “Extraction of extracellular polymeric substances (EPS) of
sludges,” J. Biotechnol., vol. 95, no. 3, pp. 249–256, May 2002, doi: 10.1016/S01681656(02)00025-1.
[135] Michel. DuBois, K. A. Gilles, J. K. Hamilton, P. A. Rebers, and Fred. Smith, “Colorimetric
Method for Determination of Sugars and Related Substances,” Anal. Chem., vol. 28, no. 3,
pp. 350–356, Mar. 1956, doi: 10.1021/ac60111a017.

106

[136] Molecular Probes Invitrogen Detection Technologies, “ATP Determination Kit (A22066).”
Molecular Probes, Inc., 2005. [Online]. Available: https://assets.thermofisher.com/TFSAssets/LSG/manuals/mp22066.pdf
[137] E. Khan, M. Awobamise, K. Jones, and S. Murthy, “Method development for measuring
biodegradable dissolved organic nitrogen in treated wastewater,” Water Environ. Res. Res.
Publ. Water Environ. Fed., vol. 81, no. 8, pp. 779–787, Aug. 2009, doi:
10.2175/106143008x370430.
[138] D. M. Moll, R. S. Summers, A. C. Fonseca, and W. Matheis, “Impact of Temperature on
Drinking Water Biofilter Performance and Microbial Community Structure,” Environ. Sci.
Technol., vol. 33, no. 14, pp. 2377–2382, Jul. 1999, doi: 10.1021/es9900757.
[139] M. L. Price, R. W. Bailey, A. K. Enos, M. Hook, and S. W. Hermanowicz, “Evaluation Of
Ozone/Biological Treatment For Disinfection Byproducts Control And Biologically Stable
Water,”

J.

Int.

Ozone

Assoc.,

vol.

15,

no.

2,

pp.

95–130,

1993,

doi:

https://doi.org/10.1080/01919519308552263.
[140] K. Chinu, M. A. H. Johir, S. Vigneswaran, H. K. Shon, and J. Kandasamy, “Biofilter as
pretreatment to membrane based desalination: Evaluation in terms of fouling index,”
Desalination, vol. 247, pp. 77–84, Oct. 2009, doi: 10.1016/j.desal.2008.12.014.
[141] H. P. Hamidi, S. L. D. Kenari, and O. D. Basu, “Simultaneous TOC and Ammonia
Removal in Drinking-Water Biofilters: Influence of pH and Alkalinity,” J. Environ. Eng.,
vol. 146, no. 8, p. 04020080, Aug. 2020, doi: 10.1061/(ASCE)EE.1943-7870.0001758.
[142] S. H. So, I. H. Choi, H. C. Kim, and S. K. Maeng, “Seasonally related effects on natural
organic matter characteristics from source to tap in Korea,” Sci. Total Environ., vol. 592,
pp. 584–592, Aug. 2017, doi: 10.1016/j.scitotenv.2017.03.063.

107

[143] J. L. Weishaar, G. R. Aiken, B. A. Bergamaschi, M. S. Fram, R. Fujii, and K. Mopper,
“Evaluation of Specific Ultraviolet Absorbance as an Indicator of the Chemical
Composition and Reactivity of Dissolved Organic Carbon,” Environ. Sci. Technol., vol.
37, no. 20, pp. 4702–4708, Oct. 2003, doi: 10.1021/es030360x.
[144] V. A. Nemani, M. J. McKie, L. Taylor-Edmonds, and R. C. Andrews, “Impact of biofilter
operation on microbial community structure and performance,” J. Water Process Eng., vol.
24, pp. 35–41, Aug. 2018, doi: 10.1016/j.jwpe.2018.05.009.
[145] S. Ikhlef and O. D. Basu, “Influence of backwash regime on biofilter performance in
drinking water treatment,” J. Chem. Technol. Biotechnol., vol. 92, no. 7, pp. 1777–1784,
2017, doi: 10.1002/jctb.5178.
[146] K. Winter, S. Mgese, E. Nicklin, and K. Maraj, “Treating and reusing polluted runoff from
an informal settlement, South Africa,” Water Pract. Technol., vol. 18, no. 4, pp. 796–809,
Apr. 2023, doi: 10.2166/wpt.2023.045.
[147] M. A. Gómez, E. Hontoria, and J. González-López, “Effect of dissolved oxygen
concentration on nitrate removal from groundwater using a denitrifying submerged filter,”
J. Hazard. Mater., vol. 90, no. 3, pp. 267–278, Mar. 2002, doi: 10.1016/S03043894(01)00353-3.
[148] D. A. Søborg, I. L. Breda, and L. Ramsay, “Effect of oxygen deprivation on treatment
processes in a full-scale drinking water biofilter,” Water Supply, vol. 15, no. 4, pp. 825–
833, Mar. 2015, doi: 10.2166/ws.2015.040.
[149] G. Naidu, S. Jeong, S. Vigneswaran, and S. A. Rice, “Microbial activity in biofilter used
as a pretreatment for seawater desalination,” Desalination, vol. 309, pp. 254–260, Jan.
2013, doi: 10.1016/j.desal.2012.10.016.

108

[150] L. Pharand, M. I. Van Dyke, W. B. Anderson, and P. M. Huck, “Assessment of biomass in
drinking water biofilters by adenosine triphosphate,” J. AWWA, vol. 106, no. 10, pp. E433–
E444, 2014, doi: 10.5942/jawwa.2014.106.0107.
[151] T. T. More, J. S. S. Yadav, S. Yan, R. D. Tyagi, and R. Y. Surampalli, “Extracellular
polymeric substances of bacteria and their potential environmental applications,” J.
Environ. Manage., vol. 144, pp. 1–25, Nov. 2014, doi: 10.1016/j.jenvman.2014.05.010.
[152] J. W. Lee, W. G. Yeomans, A. L. Allen, F. Deng, R. A. Gross, and D. L. Kaplan,
“Biosynthesis of Novel Exopolymers byAureobasidium pullulans,” Appl. Environ.
Microbiol., vol. 65, no. 12, pp. 5265–5271, Dec. 1999, doi: 10.1128/AEM.65.12.52655271.1999.
[153] C.-H. Shu and M.-Y. Lung, “Effect of pH on the production and molecular weight
distribution of exopolysaccharide by Antrodia camphorata in batch cultures,” Process
Biochem., vol. 39, no. 8, pp. 931–937, Apr. 2004, doi: 10.1016/S0032-9592(03)00220-6.
[154] C. Di Tommaso, L. Taylor-Edmonds, S. A. Andrews, and R. C. Andrews, “The
contribution of biofilm to nitrogenous disinfection by-product formation in full-scale
cyclically-operated drinking water biofilters,” Water Res., vol. 155, pp. 403–409, May
2019, doi: 10.1016/j.watres.2019.02.025.
[155] A. M. Elhadidy, M. I. V. Dyke, F. Chen, S. Peldszus, and P. M. Huck, “Development and
application of an improved protocol to characterize biofilms in biologically active drinking
water filters,” Environ. Sci. Water Res. Technol., vol. 3, no. 2, pp. 249–261, 2017, doi:
10.1039/C6EW00279J.

109

[156] I. Papineau, N. Tufenkji, P. Servais, and B. Barbeau, “Impact of Media Aging on the
Removal of Cryptosporidium in Granular Media Filters,” J. Environ. Eng., vol. 139, no. 5,
pp. 603–611, May 2013, doi: 10.1061/(ASCE)EE.1943-7870.0000672.
[157] S. E. Keithley and M. J. Kirisits, “An improved protocol for extracting extracellular
polymeric substances from granular filter media,” Water Res., vol. 129, pp. 419–427, Feb.
2018, doi: 10.1016/j.watres.2017.11.020.
[158] M. R. Keon, M. J. McKie, L. Taylor-Edmonds, and R. C. Andrews, “Evaluation of enzyme
activity for monitoring biofiltration performance in drinking water treatment,” Water Res.,
vol. 205, p. 117636, Oct. 2021, doi: 10.1016/j.watres.2021.117636.
[159] P. Schober, C. Boer, and L. A. Schwarte, “Correlation Coefficients: Appropriate Use and
Interpretation,” Anesth. Analg., vol. 126, no. 5, pp. 1763–1768, May 2018, doi:
10.1213/ANE.0000000000002864.

110

Appendices
Appendix A – Calibration curves
y = -315.57x + 1.4728
R² = 0.981

100

Concentration (mg/l)

80
60

40
20
0

-0.3

-0.25

-0.2
-0.15
-0.1
Absorbance (nm)

-0.05

0

Figure A.1: Calibration curve for COD solution

Figure A.2 ATP calibration curve for DOC at 20 mg/L (a), 50 mg/L (b), and 100 mg/L (c)
-1-

Figure A.3: Calibration curve for EPS - Proteins (a & b) and EPS - Sugar (c)

-2-

Appendix B – Influent water quality

Figure A.4: Influent water analysis report
-3-

Figure A.5: Influent water analysis report

-4-

Appendix C – Experimental results

Figure A.6:DOC concentration of influents and effluents at DOC = 20 mg/L (a) 50 mg/L (b), and 100 mg/L (c)

-5-

Figure A.7: ATP concentration of biofilter media at DOC = 20 mg/L (a), 50 mg/L (b), and 100 mg/L (c)

-6-

Table A.1 Comparison between DO and ATP concentrations of all runs
Experimental run

DO concentration (mg/L)

ATP concentration (mg/L)

R1

5±1

1.0 ± 0.1

R2

3±1

1.1 ± 0.1

R3

4±1

0.8 ± 0.1

R4

4±1

6.5 ± 1.3

R5

3±1

2.6 ± 0.5

R6

4±2

1.5 ± 0.4

R7

5±1

1.0 ± 0.2

R8

5±1

1.0 ± 0.5

R9

4±2

1.4 ± 1.0

-7-

Appendix D – Statistical Analyses
Table A.2: Regression equation
Media type
Sand

DOC removal (%) = 77.40 – 0.6575 DOC (mg/L)

GAC

DOC removal (%) = 82.00 – 0.6575 DOC (mg/L)

Sand + GAC

DOC removal (%) = 85.39 – 0.6575 DOC (mg/L)

Table A.3: Regression coefficients and model summary
Coefficients
Variables

Coef

SE Coef

T-Value

P-Value

VIF

Constant

81.26

3.62

22.44

0.000

DOC (mg/L)

-0.6575

0.0541

-12.16

0.000

1.00

GAC

0.26

2.53

-0.10

0.918

1.35

Sand

-3.87

2.57

-1.50

0.137

1.35

S

R-sq

R-sq (adj)

R-sq (pred)

10-fold S

10-fold R-sq

16.5534

65.15%

63.86%

61.56%

16.8767

61.99%

Model summary

Table A.4: Fits and Diagnostics for Unusual Observation
Obs

DOC removal (%)

Fit

Resid

Std Resid

64

58.00

11.64

46.36

2.88 R

74

51.00

15.25

35.75

2.22 R

84

59.00

19.63

39.37

2.44 R

-8-

Appendix E – Copyrights permissions

Figure A.8: Journal permission

Figure A.9: Co-author's permissions
-9-

Figure A.10: Co-author's permission

- 10 -