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Multiphysics modelling

of ow dynamics, biolm development and

wastewater

treatment in

a subsurface

vertical ow constructedwetland

mesocosm

Amin Reza Rajabzadeh a, Raymond L. Leggea, Kela P. Weber b,*aDepartment of Chemical Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, CanadabEnvironmental Sciences Group, Department of Chemistry and Chemical Engineering, Royal Military College of Canada, Kingston, Ontario K7K 7B4, Canada,

A

R

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I

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Article history:

Received

7

April

2014

Received in revised form 17 September 2014

Accepted 30 September 2014

Available online xxx

Keywords:

Constructed wetland

Computational uid dynamics

Biolm

Microbial communities

Porosity

Clogging

Wetland

hydrology

Wetland modelling

A

B

S

T

R

A

C

T

A robust computationaluid dynamics (CFD) model accounting for both spatial and temporal dynamics

of a subsurface vertical ow treatment wetland system was developed by combining uid transport,

solute transport, biokinetics, biolm development, and biolm detachment/sloughing using COMSOL

MultiphysicsTM.

The local porosity of the porous media was calculated based on the estimated biolm

development over time. The biolm development sub-model considered both organic pollutant

metabolism/degradation as a mechanism for microbial growth and the effect of uid shear stress on the

local biolmdetachment. Theinuence of biolm accumulationon thepermeabilityof theporousmedia

wasconsideredusingthe Kozeney–Carmanrelation. Thepermeability of theporousmediawasestimated

by calibrating the model against experimental tracer data that had been collected in a full water-

recirculation batch operation. As the biolm developed from start-up, removal ef ciency of readily

biodegradable organic matter improved during the rst 10 weeks of operation while no signicant

change was predicted after 10 weeks. A dead-zone region was predicted near the bottom of the system

opposite to theoutowport after 100 days of operationattributed to exponential biolm growthand low

biolmdetachment rates.A decrease of95%in theadvective transportof organic matterin thedead-zone

region occurred after 280 days of operation. No appreciable biolm accumulation was predicted in the

region near the outlet due to the high uid shear stress acting on the biolm surface. The averaged

porosity of the mesocosm decreased by 6.6% after 365 days of operation while the local porosity in the

dead-zone region decreased by 71%. Good agreement was found between the averaged porosity of the

entiresystem andexperimentaldata.This is therst modelto integratehydrodynamics, solutetransport,

biokinetics relating to pollutant removal, biolm development, and biolm detachment into a

comprehensive model. Although computationally intensive, this is therstmodel to predict bio-clogging

processes in a spatial manner similar to what would be realistically expected.

ã 2014 Elsevier B.V. All rights reserved.

1. Introduction

Constructed wetlands (CWs) are engineered ecosystems

comprised of vegetated media and colonized microorganisms that

employ natural processes that can be used to assist in treating

wastewater (Vymazal and Kröpfelová, 2008). Constructed wet-

lands are a proven and effective technology to remove organic

matter, nutrients, pathogens, and other pollutants from wastewa-

ter (Garcia et al., 2010).

One of the operational problems associated with CWs is

clogging of the granular media (Caselle-Osorio et al., 2007;

Knowles et al., 2011 Knowles et al., 2011). The main contributors

to clogging are accumulation of particulate matter and biolm

development, with biolm development being largely the result of

microbial attachment and growth on the support media (Langer-

graber et al., 2003). Clogging has various effects on the

hydrodynamic characteristics of CWs by lowering the porosity

and permeability in specic areas of a wetland bed (Kadlec and

Wallace, 2008; Pedescoll et al., 2009, 2011; Sharp et al., 1999;

Weber and Legge 2011). Changes in the hydrodynamic character

results in preferential water ow along the treatment wetland and

dead-zone regions decreasing overall contact time and thus

treatment ef ciency (Garcia et al., 2005; Persson et al., 1999;

Platzer and Mauch,1997). Previous work has focused on theoretical

models that take into account the effect of suspended solids on

clogging of the porous media without consideration of the effects* Corresponding author.

E-mail address: [email protected] (K.P. Weber).

http://dx.doi.org/10.1016/j.ecoleng.2014.09.122

0925-8574/ã 2014 Elsevier B.V. All rights reserved.

Ecological Engineering 74 (2014) 107–116

Contents

lists

available

at

ScienceDirect

Ecological

Engineering

journal homepage: www.else vie r .com/locate/e coleng

mailto:[email protected]:[email protected]://dx.doi.org/10.1016/j.ecoleng.2014.09.122http://dx.doi.org/10.1016/j.ecoleng.2014.09.122http://dx.doi.org/10.1016/j.ecoleng.2014.09.122http://dx.doi.org/10.1016/j.ecoleng.2014.09.122http://dx.doi.org/10.1016/j.ecoleng.2014.09.122http://dx.doi.org/10.1016/j.ecoleng.2014.09.122http://dx.doi.org/10.1016/j.ecoleng.2014.09.122http://dx.doi.org/10.1016/j.ecoleng.2014.09.122http://dx.doi.org/10.1016/j.ecoleng.2014.09.122http://dx.doi.org/10.1016/j.ecoleng.2014.09.122http://dx.doi.org/10.1016/j.ecoleng.2014.09.122http://dx.doi.org/10.1016/j.ecoleng.2014.09.122http://www.sciencedirect.com/science/journal/09258574http://www.elsevier.com/locate/ecolenghttp://www.elsevier.com/locate/ecolenghttp://www.elsevier.com/locate/ecolenghttp://www.elsevier.com/locate/ecolenghttp://www.elsevier.com/locate/ecolenghttp://www.elsevier.com/locate/ecolenghttp://www.elsevier.com/locate/ecolenghttp://www.elsevier.com/locate/ecolenghttp://www.sciencedirect.com/science/journal/09258574http://dx.doi.org/10.1016/j.ecoleng.2014.09.122http://dx.doi.org/10.1016/j.ecoleng.2014.09.122mailto:[email protected]://crossmark.dyndns.org/dialog/?doi=10.1016/j.ecoleng.2014.09.122&domain=pdf


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of biolm development (Blazejewski and Murat-Blazejewska,

1997; Kawanishi et al., 1990; Langergraber et al., 2003). These

models result in the overestimation of clogging time (Langergraber

et al., 2003), possibly due to biolm dynamics being ignored in said

models.

Good engineering design of CWs requires insight into the

internal chemical and biological processes as well as an

understanding of the hydrological characteristics of the system.

Tracer experiments have been used to study the effect of design

parameters on the hydrodynamic behavour of CWs (Huang et al.,

2005; Molle et al., 2006; Suliman et al., 2006, 2007). Tracer

experiment methodologies can be relatively expensive and often

impossible to perform in eld situations; therefore, mathematical

models are increasingly sought to study the hydraulic character-

istics of many subsurface environments, including CWs. A variety

of analytical and numerical models are available to predict water

and solute transport processes in porous media. Analytical

solutions of mass transport are limited to simplied transport

systems with linear reaction rates (Higashi and Pigford, 1980; van

Genuchten, 1985). Since a large number of physicochemical and

biological processes occur simultaneously in CWs, analytical

models provide limited insight into their performance aspects.

As a result, mechanistic models with numerical solutions are

required to capture the complexity of CWs (Langergraber andŠimùnek, 2005, 2012; Llorens et al., 2011; Mburu et al., 2012;

Samsó and García, 2013) rather than simple rst order decay

models (Kadlec and Wallace, 2008; Stein et al., 2006; Tomenko

et al., 2007). First order decay models are site specic and neglect

the effect of important factors such as ow behavior, microbial

growth/biolm development, and solids accumulation on the

operation of CWs (Garcia et al., 2010). The majority of reported

mechanistic models for CWs have been developed based on

advanced activated sludge biokinetic models (ASMs) proposed by

Henze et al. (2000) to predict the biochemical transformation and

elimination of dissolved organic matter, nitrogen and phospho-

rous. A macroscale biolm sub-model was considered in the bio-

kinetic model of Samsó and García (2013) to simulate the

development of different microbial communities in CWs, whichwas not considered in earlier models proposed by Langergraber

and Šimùnek (2005, 2012),),Llorens et al. (2011), and Mburu et al.

(2012). The biolm sub-model accounted for the pore volume

reduction that results from biolm growth and accumulation of

inert solids. Although biolm development has been reported to

signicantly affect the hydrodynamic characteristics of porous

media, constant hydrodynamic parameters were applied in their

proposed model. The work resulted in a proposed cartridge theory

for clogging by Samsó and García (2014). The model accounted for

the interaction between the bacterial community and the

accumulation of inert solids. Two linear negative feedback

functions were introduced to the bacteria growth rate expression

to account for limitless growth of bacteria and porosity reduction

as

a

result

of

solids

accumulation.

The

solids

accumulation

limitedavailable space and substrates for bacterial growth. Results

suggested that the zone containing an active microbial community

moves toward the outlet of the subsurface horizontal ow wetland

as a result of progressive clogging with inert solids. Although the

cartridge model did not, from a hydrodynamics perspective, give a

prediction similar to what is seen in practice, the model

represented a progression towards linking biolm and solids

accumulation to constructed wetland performance and function.

The effects of ow behavior and local shear stress on biolm

detachment, which are governed by biolm development and pore

volume reduction rate, were neglected in the model proposed by

Samsó and García (2014) possibly contributing to the moving-

cartridge type overestimation of clogging in their model.

The overall objective of this work was to explore the effect of

biolm development on the wetland’s hydrodynamic character-

istics to ultimately better understand water treatment variation

and dynamics for full scale CWs. A robust computational uid

dynamics (CFD) model was developed that accounted for spatial

and temporal dynamics within a treatment wetland mesocosm by

combining uid transport, solute transport, biokinetics, biolm

development, and biolm detachment sub-models using

COMSOL 1 Multiphysics.

2.

Materials

and

methods

2.1. Experimental set-up

The wetland mesocosms used to calibrate and validate the

model have been described in detail by Weber and Legge (2011).

The wetland mesocosms consisted of a PVC cylinder (80 cm tall;

25 cm diameter) lled with pea gravel (average equivalent

diameter of 1 cm, 80% limestone) and operated to 70 cm with

water (Fig. 1). Water was pumped (a) from the lower portion of

the wetland to the upper region (b), ensuring continuous cycling

of the feed as it moved downwards (c). The wetland was tted

with an injection (d) and sample (e) port for sampling or tracer

injections. The mesocosms could be drained using the bottomport (f). Water was fed to the top of the system 10 cm below the

surface of the gravel via a perforated PVC pipe. The top 10 cm of

gravel simply covered the feed inlet pipe (no water passed

through this top 10 cm region). The ow regime for modeling

purposes in these systems can therefore be described as saturated

(top 10cm ignored as no ow occurs here). Oxygen was

constantly supplied to these systems through mixing and

diffusion at the sampling port during water cycling from the

bottom to the top of the system and wasconsistently measured as

4–5 mg/L. Each week the mesocosm was lled with fresh

simulated wastewater and operated under constant recycle for

1 week. The simulated wastewater consisted of 1g/L molasses,

0.049 g/L urea, 0.0185 g/L NH4H2PO4, yielding a COD of 500 mg/L

and a COD:N:P ratio of 100:5:1 (Weber and Legge, 2011). After1 week the mesocosm was drained before rell and the porosity

was measured (measured as the amount of water required to ll

a

d e

b

c

f

7 0 c m

25 cm

Fig. 1. Unplanted wetland mesocosm schematic. Water was circulated via a small

centrifugal pump (a) to the mesocosm (b) and allowed to percolate through the pea

gravel bed and collected at the bottom (c). An atmosphere exposed port served as an

injection (d) and sampling (e) point. A drainage port was located near the bottom (f)

for mesocosm drainage.

108 A.R. Rajabzadeh et al. / Ecological Engineering 74 (2014) 107 –116


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the systems after a complete drain). For calibration of the

hydrodynamic characteristics of the model, a tracer study was

performed using 10 mL of a 200 g/L sodium bromide solution. The

tracer concentration was measured based on the conductivity of

the solution passing through the sampling port over time. The

removal of organics from the simulated wastewater was

evaluated using chemical oxygen demand (COD) according to

Standard Method 5220D. Unplanted wetland mesocosms were

considered for model calibration to identify the porosity change

with biolm development in an environment not affected by

plant root development, root exudates or root degradation

products.

2.2.

Model

development

2.2.1. Governing equations

A variety of analytical and numerical models are available to

predict water and solute transfer processes in porous media. The

free and porous media ow interface in COMSOL (COMSOL, Inc.,

USA) was used to estimate ow behavior in a saturated porous

media. This interface uses the Navier–Stokes equations for

describing the

ow

in

open regions,

and the

Brinkman

equationsfor the ow in porous regions. Darcy’s law has been used widely

in the literature to describe velocity and pressure proles of ow

in porous media (Liolios et al., 2012; Samsó and García, 2013;

Llorens et al., 2011). One of the main driving forces for using the

Darcy equation is its ease of use and lighter computational

requirements in numerical models. In the present study the

authors opted for a more computationally demanding, yet more

physically accurate description of uid ow. Darcy’s law provides

a linear relationship between hydraulic gradient and velocity of

ow in porous media by ignoring the viscous and inertia effects

to the uid ow. For the situation considered here, water is

owing through a coarse media (pea gravel). The relationship

between velocity and hydraulic gradient can be non-linear for

ow through coarse sand and gravel, therefore the Darcy

equation was not used (Reddy and Rao, 2006; Crites et al.,

2014). The Brinkman model extends Darcy’s law to include the

viscous transport and inertia term in the momentum balance.

The dependent variables in the Brinkman equations are the

directional velocities and pressure. The ow in porous media is

governed by a combination of the continuity equation and

momentum balance equation, which together form the Brink-

man equation:

@

@t e pr

þ r ruð Þ ¼ Q br (1a)

r

e p

@u

@t þ ðurÞ

u

e p

¼ r p

þr 1

e pm ruþ ðruÞT

n

2

3mðr uÞI

mk þ Q br

uþ F (1b)

where m denotes the dynamic viscosity of the uid (kg/(m s)), u

is the velocity vector (m/s), r is the density of the uid (kg/m3), p is

the pressure (Pa), e p is the porosity, k is the permeability of the

porous media (m2), and Q br is a mass source or mass sink

(kg/(m3 s)). Inuence of gravity and other volume forces was

accounted for via the force term F (kg/(m2 s2)). A single phase liquid

ow was considered in the model because the mesocosms were

operated in a saturated vertical ow conguration. The reaction

and transport of chemical species by diffusion and advection were

calculated using Eq. (2).

@c

@t þ

urc ¼

r Drc ð Þ

þ

R

(2)

where c is the concentration of the species (g/m3), D denotes the

isotropic molecular diffusivity (m2/s), R is a reaction rate

expression for the species (g/(m3 s)), and u is the velocity vector

(m/s) from the solution of Eq. (1).

Non-uniform triangular mesh elements with different reso-

lutions were adopted with the mesh density being larger near the

edge boundaries. Dense boundary layer mesh elements were

created in the normal direction of the boundaries to capture the

thin mass boundary layers close to wall boundaries. COMSOL

Multiphysics applies nite element principles to discretize the

partial differential equations into a set of differential algebraic

equations. The direct solver MUMPS (multifrontal massively

parallel sparse direct solver) was used to solve the resulting linear

system. MUMPS solves linear systems with the multi-frontal

method and direct LU (lower-upper) factorization of the sparse

matrix. The simulation was assumed to converge when the

weighted absolute residual norm was less than 105. The model

was run to simulate a one year start-up period. Each modelled

week took approximately 30 min in real-time on a 6.00GB RAM,

CoreTM i5-2.5 GHz laptop.

Table 1

Coef cients of biokinetic model (Langergraber and Šimùnek, 2005) and physical properties used in the CFD model.

Name Expression (units) Description

mmax,H 6 (1/d) Maximum growth rate

K s 1000 (g/m3) Half maximum rateY H 0.63 True yields (biomass produced/substrate consumed)

D 1 104 (m2/d) Substrate diffusion in water

S S0 300 (g/m3) Initial readily biodegradable organic matter

C S0 0 (g/m3) Initial slowly biodegradable organic matter

C I 175 (g/m3) Inert chemical oxygen demand

X H0 0.01 (g/m3) Initial heterotroph concentration

rw 997 (kg/m3) Water density

mw 0.000843 (Pa s) Water viscosity

e0 0.365 (1) Initial void fraction

rbulk 80 (kg/m3)

Biolm density

Dcol 0.25 (m) Mesocosm diameter

Lcol 0.7 (m) Mesocosm height

dp 1 (cm) Average gravel diameter

M 404 (1/m) Specic surface area

K 4.5 109 (m2) Intrinsic permeability

k 452 (m/d) Hydraulic conductivity

A.R. Rajabzadeh et al. / Ecological Engineering 74 (2014) 107 –116 109


4/10

Dispersion in porous media occurs due to varying pore sizes,

varying ow velocity in pore spaces, and tortuous ow paths

(Selker et al., 1999). In large scale (macroscopic) models average

velocity is used to describe advection, while dispersion is

accounted for using a correction term called dispersivity.

Dispersivity is unique to each specic modelling situation and

needs to be t (usually through tracer studies and model

calibration). Because the present study used a computationally

robust and heavily discritized geometry actual local velocities are

calculated (water velocity and direction is calculated in each of the

1906 elements), therefore no bulk correction/tting factor, such as

dispersivity, is required. Dispersive phenomena is well described

using the fundamental approach applied here as shown in Fig. 2

(tracer test tting). For a fundamental description and a direct

comparison of microscopic (present study) and macroscopic (eld

scale) dispersive ux modeling see Selker et al. (1999) or other

similar fundamental porous media literature.

2.2.2.

Chemical

and

biological

reactions

in

the

biokinetic

model

The processes considered in the biokinetic model were

hydrolysis, mineralization of organic matter, and a lysis process

for

microorganisms

(Langergraber

and Š

imùnek,

2005;

Henzeet al., 2000). Heterotrophic bacteria ( X H) consume readily

biodegradable organic matters (S S) and oxygen to grow aerobically.

A Monod type function with a limiting factor of organic matter was

used to express the growth of the heterotrophs. Oxygen was in

excess throughout the mesocosms studied here due to the vertical

recycle operation, and water mixing at the sampling port. For

systems with multiple limiting factors such as oxygen, organic

matter, and other substrates/nutrients, Monod dual-substrate

limitation kinetics are recommended (Wynn and Liehr, 2001;

Brovelli et al., 2011).

dX Hdt

¼ mmax;HS s

K s þ S s X H (3)

where mmax,H is the maximum aerobic growth rate, S s is readily

biodegradable organic matter concentration (a portion of the total

COD present), and K s is the half maximum rate. Table 1 summarizes

the coef cients used in the biokinetics model. Lysis of the

heterotrophic organisms results in slowly biodegradable organic

matter. A simple linear relation was dened to represent this

process.

dX H ¼ bina;H X H (4)

where bina,H represents the lysis rate coef cient.

Hydrolysis of slowly biodegradable organic matters (C s) to

readily biodegradable organic matter was considered in the model

using Eq. (5).

dSsdt

¼ khCs=XH

kx þ Cs=XHXH (5)

where kh and kx are hydrolysis rate constant and saturation/

inhibition coef cient for hydrolysis, respectively which are listed

in Table 1. Readily biodegradable organic matter removal was

therefore dened as

dS sdt ¼

1

Y Hmmax;H

S sK s þ S s

X H (6)

where Y H is yield coef cient representing the stoichiometric link

between biomass growth and organic matter (OM) consumption

and is dened as biomass production/OM consumption (Langer-

graber and Šimùnek, 2005).

Fig.

2.

Simulated

and

experimental

tracer

curves

(A)

and

spatial

distribution

(B)

for

a

tracer

concentration

(1

g/L

initial

concentration)

as

a

function

of

time.



5/10

The spatial growth of microorganisms in the mesocosm

geometric domain was dened using a distributed ODE interface

in COMSOL. The interface allowed for the integration of the

ordinary differential equations of 3 and 4 for microbial growth in

any region of the mesocosm as a separate phase (representing

biolm/solid media) from the uid phase while the hydrolysis of

slowly biodegradable organic matters and the removal of readily

biodegradable organic matter (Eqs. (5) and (6) were dened in the

uid ow phase (Eq. (2)).

2.2.3. Biomass detachment rate

Rittmann (1982) proposed that biolm detachment (or loss) is

governed by uid shear stress acting on the biolm surface and

developed an experimentally based equation to calculate biolm

detachment. According to this equation, an increase in the ow

rate causes a decrease in biolm volume surrounding substrate

media and thus an increase in local pore volume. The effect of ow

behavior on the biolm detachment rate was considered in the bio-

clogging sub-model via Eq. (7) proposed by Rittmann (1982).

Fig. 3. Velocity transects along the y-axis (A) and x-axis (B) after 8 weeks.



6/10

bdet;H ¼ 2:29 106 mu 1 eð Þ

3

dp2e3M

!0:58(7)

where bdet,H is the biomass detachment rate constant, u is the local

velocity (cm/d),e is local porosity, dp is averaged spherical gravel

diameter (cm), M is specic surface area of the porous medium

(1/cm) andm is viscosity in g/cm/d. A uniform gravel size with a

spherical shape was used for determining M .

2.3.

Hydrodynamic

characteristics

of

the

mesocosm

Biolm was assumed to consist of bacteria as a solid biolm

phase growing on the gravel within the pores. The local porosity of

the porous media was calculated at each time interval based on the

estimated biolm concentration ( X H ) using Eq. (8). The local

volume of the biolm was calculated over time from the calculated

biomass concentration (XH) known biolm density (rBiolm), and

initial void fraction (e0) (Eq. (8)). The velocity and pressure prole

was updated over time by solving Eq. (1)a and b.

e x; y; tð Þ ¼ e0 1 X H x;y; zð Þ

rBioflim

(8)

Finally, the inuence of biolm growth on the permeability of

the mesocosm which resulted in reduced porosity was considered

using the Kozeney–Carman relation in the following form

(Blazejewski and Murat-Blazejewska, 1997):

k

x; y; t ð Þ ¼ kt ¼0e x; y; zð Þ

e t ¼ 0ð Þ

3 1 e x; y; zð Þ1 e t ¼ 0ð Þ

2(9)

The Kozeny–Carman relation is the most widely used semi-

analytical equation in the porous media literature (developed from

capillary models) relating the permeability of a media to its

porosity, pore shape factor, and kinematic viscosity. The equation is

mainly used for homogenous granular porous media at interme-

diate porosities (Carman, 1937) and proved to be applicable in thecurrent modelling study.

COMSOL is a computationally powerful platform and was

selected due to its ability to model local hydrodynamic properties

such as water velocity. Mass balances are calculated between the

1906 mesh elements, and these elements are spatially distributed

in 2 dimensions. Using this format water velocity could be related

to shear stress and biolm detachment/sloughing in each

individual element. The sloughing of biolm then feeds back into

the hydrodynamic model recalculating water velocity based on the

new available pore space. If OM is then able to move again to this

local element, biolm growth can re-occur, thus decreasing the

pore space available. It is through these interacting models for

hydrodynamics, solute transport, biokinetics, biolm growth, and

biolm sluoghing that the multiphysics model is able to spatially

and temporally describe the bio-clogging phenomena.

2.4. Boundary conditions

At the inlet of the mesocosm, the ow was assumed to be fully

developed and a parabolic ow was specied. The maximum

velocity (vz,max) of the inlet parabolic ow was computed from the

feed owrate and the dimension of the inlet pipe. At the mesocosm

outlet the pressure was assumed to be atmospheric pressure (P out),

as an open sampling port provided atmospheric exposure and

water mixing. A periodic boundary condition was considered for

the OM at the inlet and outlet to ensure mass continuity. A no slip

boundary condition was dened at the mesocosm wall.

3.

Results

and

discussion

The permeability of the gravel media was estimated by

calibrating the model against experimental tracer data (Fig. 2)

with readings taken at the sampling port while the system is run in

total recirculation mode. Fig. 2(A) shows the tracer concentration-

time distribution with consecutive peaks detected at each passage

corresponding to the circulation time. The peaks are due to local

tracer concentration peaks which occur before the solute is

completely mixed within the mesocosm. The mean retention time

and standard deviation of the tracer distribution were 248 s and

88 s, respectively. The spread of each peak with a standard

deviation about the mean residence time reects the extent to

which the ow deviates from theoretical plug ow. Generally a

peak with a large standard deviation reects the presence of

channeling or the presence of a re-circulating zone (Persson et al.,1999). The maximum tracer concentrations for the rst and second

peaks were observed at approximately 220 s and 440 s, respec-

tively. Tracer concentration leveled off after 700 s representing a

completely mixed system. Fig. 2(A) shows that the model

described the experimental tracer data with an acceptable degree

of accuracy. The mean squared error (MSE) between experimental

Fig.

4.

Simulated

biolm

concentration

(left)

and

bio

lm

concentration

surface

plot

in

the

mesocosm

after

365

days

(right).



7/10

and estimated tracer data and R2 were 7.9 104 and 0.99,

respectively. Fig. 2(B) shows the spatial distribution of tracer along

the mesocosm in response to a tracer input and steady state ow

conditions, it can be noted that near the bottom of the mesocosm

system water ow is directed towards the outlet, leaving the

general area opposite the outlet (left hand side given Fig. 2(B))

untouched by the tracer path. Fig. 3 outlines x-axis and y-axis

velocity transects at an arbitrary time of 8 weeks, and exemplies

the ability for this model to calculate local (microscopic) velocities

in each mesh element.

Fig. 4 showsbiolm development as a concentration (mg/cm3)

within specic regions of the mesocosm over time. The initial

concentration (at time zero) of the biolm was set to a value close

to zero (1 105mg/cm3). Fig. 4 focuses on results for three regionsin the mesocosm with the trends of biolm growth compared with

the spatially averaged values ð1=x y

Z X 0

Z Y 0

XHdxdy. These three

regions were chosen and evaluated based on the expected

difference in development due to spatial placement. Region

1 was at the outlet where a consistently large water velocity

was expected. Region 3 was selected based on its placement in an

area expected to see a lower water velocity; intuitively, Region

3 runs the risk of becoming a dead-zone due to its placement with

respect to the outlet region (as can be seen in Figs. 2 and 3B).

Region 2 was placed in the middle of the mesocosm as it was

predicted to be more indicative of an averaged mesocosm region.

The spatially averaged biolm concentration (whole systemaverage) increased linearly up to 150 days and leveled off after

approximately 300 days. Biolm concentration in Region 3 grew

exponentially up to 200 days, and then leveled off after 280 days.

The biolm concentration at Region 3 was approximately 3 times

greater than the averaged biolm concentration due to the low

shear stress and therefore limited biolm detachment. At the

outlet (Region 2), biolm detachment was high due to the high

uid shear stress acting on the biolm surface. Biolm concen-

trations of 0.1 and 32.9 mg/cm3 gravel were observed after

365 days at Regions 1 and 3, respectively. This represents

approximately a 330 times greater biolm accumulation in the

“dead-zone” region (Region 3) in comparison to the high shear

outlet region (Region 1). The biolm concentration value at Region

2 was close to the averaged value (3.0 mg/cm3). The balance

between biolm growth and detachment forces was the main

factor determining the biolm distribution in the mesocosm. The

velocities were 2.3 102m/s, 5 103m/s, and 4.5 104m/s at

regions 1–3, respectively reecting signicant differences in the

biolm development in the same regions. According to Eq. (7),

biolm detachment rate was proportional to the local ow velocity

to the power of 0.58 (Rittmann, 1982).

Local porosity of the media was calculated based on the local

biolm concentration and biolm density (Eq. (8)). A biolm

density of 80 kgdrymass/m3was selected in the calibration process to

best t the experimental porosity data of Weber and Legge (2011)

which is within the range of values reported in the literature of

8.8–107.8 kg/m3 (Zhang and Bishop, 1994), 10–140 kg/m3 (Bishopet al., 1995), 60 kg/m3 (Wichern et al., 2008), and 25–67 kg/m3

(Kwok et al., 1998). The simulated porosity was within acceptable

agreement with the experimental data with an R2 and MSE of

0.89 and 7.8 106, respectively.

Fig. 5 summarizes the porosity changes over time. Trends for

the spatially averaged (overall mesocosm porosity) and experi-

mental data match quite closely over the development period. A

progressive reduction of porosity over time was observed due to

biolm development on the gravel followed by a leveling-off after

Fig. 5. Simulated and experimental porosity (left) and porosity surface plot in the mesocosm after 365 days (right).

0

0.0001

0.0002

0.0003

0.0004

0.0005

0.0006

0.0007

0.0008

0 100 200 300 400

A d v e c v e F l u x ( g / m 2 / s )

Days

Region 1

Region 2

Region 3

Fig.

6.

Simulated

advective

ux

of

organic

matter.



8/10

300 d of treatment. The averaged porosity decreased by 6.6% after

365 d of operation. Simulated data showed that pore volume

reduction occurs with various rates in different regions in the

mesocosm. The local porosity of media for Region 3 decreased by

71% from the initial value of 0.365–0.104 after 365 d compared to

0.22% decrease for Region 1 (i.e., close to no change). Good

agreement can be seen between experimental and modelled

results for porosity. Samsó and García (2014) predicted a moving

section of clogged pore space. Here a more isolated "dead-zone" is

predicted which is in-line with expectations and observations.

Large decreases in porosity were observed in Region 3 between

150 and 250 days. This was attributed to the combination of

exponential

growth

of

heterotrophic

bacteria

in

the

biolm

(Fig.

4)and a lack of shear stress for biolm detachment. The exponential

growth of biolm slowed after 150 d due to the limitations of local

OM required for the growth of heterotrophic bacteria (OM ux to

the region was limited). The growth of biolm was essentially

governed by advective transport of OM to the local area. The

diffusive transport of OM was 3–4 orders of magnitude lower in

this region than the advective transport in other regions. The

advective transport of OM in Region 3 (Fig. 6) decreased by 82%

after 365 days of operation from 1.5105g/(m2 s) to 2.7 106g/

(m2 s) which helped decrease biolm growth after 280 days. The

advective ux of OM depends signicantly on the ow behavour

and local permeability of the media (Fig. 7). Region 3 in the

mesocosm exhibited a signicant decrease in permeability during

the

rst

200

d

due

to

local

biolm

development.

The

effect

of biolm growth on the permeability of the media was considered in

the model through Eq. (9). Over time permeability of the

mesocosm in Region 2 decreased by 22% while the permeability

in Region 1 remained unchanged.

Effect of biolm development on the removal ef ciency of

readily biodegradable organic matter (OM) is presented in Fig. 8. As

described in the methods section, the mesocosm was drained,

relled with fresh simulated wastewater, and operated in a recycle

mode each week. Fig. 8 represents the OM concentration in the

ef uent for the rst 6 h of each 1-week treatment for different

weeks. Based on the results, the time required for removal of all the

OM for a one week batch period depended on the treatment week.

The OM was removed in less than 5 h during the second week, with

decreasing time requirements as the mesocosms developed.

Limited organic matter removal data is available over the same

modelled time period. Week 5 experimental OM data is included in

Fig. 8 for comparison. A general agreement between experimental

and modelled data can be seen with the exception of the 1 h data

point. With limited data it is dif cult to speculate as to the reasons

for this time point discrepancy, however the general time

requirement for OM removal is reasonably calculated. The time

required to remove the OM during the tenth treatment week was

about 3 h which was 31% faster than that of week 2. No signicant

change in the OM removal curves was observed after 10 weeks of

operation. Slightly different behavior was estimated for the rst

week of operation (data not shown), the OM was estimated to be

consumed

after

30

h

of

operation

due

to

the

low

initial

heterotro-phic bacteria concentration (0.01 g/m3) in the mesocosm.

The model developed in this work has implications for large-

scale subsurface treatment wetlands where biolm development

could lead to preferential water ow or clogged regions in the

wetland. Preferential water ow could result in dead zones and

short circuiting or channelling which will have negative effects on

the treatment ef ciency of the system as a whole. Therefore, it is

extremely important to assess, understand, and prevent conditions

which lead to channeled ow in CWs and to consider them in the

development of models for full scale systems.

Fig. 7. Simulated normalized permeability (left) and normalized permeability surface plot in the mesocosm after 365 days (right).

0

50

100

150

200

250

300

350

0 1 2 3 4 5 6

R e a d i l y b i o d e g r e d a b l e o r g a n i c m a e

r

( m g / L )

Hours

Week 2Week 3

Week 5

Week 10

Week 20

Week 5_Exp. data

Fig.

8.

Effect

of

biolm

development

on

the

OM

removal.



9/10

For simplicity, and the objective being the focus of connecting

hydrological/biolm interactions and both spatial and temporal

analysis, the biochemical transformation and elimination of

dissolved nitrogen and phosphorous were not considered in the

current model. The effect of temperature, evapotranspiration, and

rainfall on the ef uent pollution concentration should also be

considered in a future version of the model, as they play an

important role on the water balance of CWs (Galvão et al., 2010;

Langergraber, 2008).

The microbial metabolic activity within a biolmand the rate of

growth therein is controlled by the concentration of substrates that

are available in the biolm (Picioreanu et al., 2004). Substrates are

transported by diffusion from the bulk uid to the biolm and are

consumed by the biomass. Due to differences in transport of

substrates and the resulting penetration prole, it is expected that

biolm microorganisms are exposed to different substrate con-

centrations, depending on their location in the biolm (Ebert,

2006). This also has not been captured in the subject model,

however could be added to future models through a biolm sub-

model. In a separate study by Murphy et al. (2014), the authors

successfully captured biolm diffusion dynamics in a model

developed to predict ammonium diffusion into biolm, nitrica-

tion reaction rates, and diffusion of nitrate out of biolms. Further

investigation and development of a biolm sub-model still needsto occur before such dynamics can be accurately added to the

model of the present study.

One of the rate-limiting components that could control biolm

growth is oxygen concentration. Some of the biological degrada-

tion processes, such as organic matter degradation and nitrica-

tion, require aerobic conditions (Langergraber, 2007). The lack of

oxygen deeper in the biolm (Bishop et al., 1995) may produce a

stable anaerobic environment (Picioreanu et al., 2004; Ebert,

2006). The biolm sub-model developed in this work will be

modied in the future work to describe some of the heterogeneous

characteristics of biolms and the resulting effect on the

biokinetics. Although oxygen was in excess throughout the

mesocosm, it is believed that oxygen could become one of the

limiting components in some regions (i.e., dead zones) andtherefore the multiple-substrate Monod equation (Wynn and

Liehr, 2001 Sun et al., 1999; Brovelli et al., 2011) is suggested as a

useful approach.

The accumulation of particulate matter (PM) in the media was

not considered in the current model because there was no PM in

the feed wastewater. Although biolm detachment contributes PM

to interstitial water, the mesocosms were completely drained each

week, and therefore the effects of PM was assumed to be negligible.

The experiments were specically designed to allow for this

quantication of the inuence of biolm development on porosity

and other hydrological characteristics.

One of the benets of the model was its ability to estimate

pollutant uxes in different areas spatially and temporally. With

the

use

of

this

feature,

the

effect

of

emerging

contaminants

such

asantimicrobials on the development, function and stability of

bacterial communities is planned by estimating the contaminant

contact time for different regions. Exposure to antimicrobials has

adverse effects on biolm development, the ability for the bacteria

to attach to the bed media, and the ability for standard life-

sustaining metabolic processes (Weber et al., 2011). Future work

will incorporate available lab scale antibiotic exposure data into

the treatment wetland model to gain an understanding of the

effect and fate on full scale operational systems.

Many vertical ow constructed wetland systems are now

operated using drain/ll cycles, have partially saturated conditions

near the top sections, or include aeration piping for improved

treatment performance (Nivala et al., 2013). This model considers

saturated

ow

only.

If

a

partially

saturated

ow

regime

is

encountered in the top sections of a vertical ow system the

Richard’s equation can easily be implemented in COMSOL and

connected to the saturated regime, this would however, require

two separate model geometries to be connected by an additional

boundary. Adding plants into the current model would not be a

simple task. Using COMSOL there is an opportunity to physically

add roots as actual geometries within the model, no ow boundary

conditions would then be set around all root geometries. The

authors have not attempted to add such a complexity to the current

model, however extended runtimes would be anticipated. Addi-

tion of plants experimentally has been shown to increase

dispersion in these types of systems (Weber and Legge, 2011); it

would be expected for the same to occur if plant geometries were

added into the current model as described.

Again it is only through the calculation of these local (in each

element) conditions that both spatial and temporal dynamics of

biolm growth was captured. No other model in the constructed

wetland literature has gone to such an extent to describe these

interacting dynamics. This is the rst model to predict bio-clogging

processes in a spatial manner similar to what would be realistically

expected. This model also marks the initiation of a method to

investigate different design aspect ratios, and how this may affect

hydrological development and contaminant transfer/treatment in

treatment wetlands.

4.

Conclusions

In this study ow dynamics, biokinetics, biolm development,

and biolm detachment was incorporated into a CFD model to

study the dynamic effect of biolm development on the

hydrodynamic characteristics of a subsurface vertical ow

constructed wetland mesocosm. The biolm submodel

accounted for organic matter metabolism/degradation as a

mechanism for microbial growth and the effect of local uid

shear stress on biolm detachment. The effect of media-related

biolm development on the porosity and permeability of a

vertical ow constructed wetland mesocosm is highlighted.

Biolm development caused a preferential ow in the mesocosm

and decreased the local porosity in a dead-zone region by 71%

after 365 days of operation while the average porosity decreased

by only 6.6%. The porosity of media in the region near the outlet

remained unchanged after 365 days of treatment due to the high

biolm detachment rate. The local development of biolm in the

dead-zone region and the region near outlet was controlled by

the concentration of OM required for bacterial growth and uid

shear stress, respectively. The consumption rate of OM in the

mesocosm was found 31% faster in the 10th week than the

second week of operation due to higher biolm development

and remained approximately unchanged after 10 weeks. The

simulated average porosity was in agreement with the experi-

mental data with the R 2 and MSE values of 0.89 and 7.8 106,

respectively. Realistic bio-clogging processes where predicted,however the model was the most computationally heavy to be

yet reported in the constructed wetland literature. With a few

modications, the model has potential for application to large

scale treatment wetlands to assess, understand, and minimize

bio-clogging.

Acknowledgements

Support from NSERC in the form of a Discovery grants to RLL and

KPW and from ORF in the way of funding from the Centre for

Control of Emerging Contaminants (CCEC) to RLL is gratefully

acknowledged. We would also like to thank Dr. Michael Hulley for

his

insight

during

manuscript

revisions.



10/10
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