transport of escherichia coli through variably saturated sand columns and modeling approaches

ology 93 (2007) 2–20www.elsevier.com/locate/jconhyd

Journal of Contaminant Hydr

Transport of Escherichia coli through variably saturated sandcolumns and modeling approaches

Guangming Jiang a,⁎, Mike J. Noonan b, Graeme D. Buchan c, Neil Smith b

a College of Environmental and Resource Sciences, Southwest University of Science and Technology, Mianyang 621002, PR Chinab Agriculture and Life Sciences Division, P. O. Box 84, Lincoln University, Canterbury, New Zealand

c Centre for Soil and Environmental Quality, Lincoln University, New Zealand

Received 21 May 2006; received in revised form 10 January 2007; accepted 15 January 2007Available online 26 January 2007

Abstract

A sand column leaching systemwith well-controlled suction and flow rate was built to investigate the effects on bacterial transportof air–water interface effects (AWI) correlated to water content, particle size, and column length. Adsorption of Escherichia colistrain D to silica sands was measured in batch tests. The average % adsorption for coarse and fine sands was 45.9±7.8% and 96.9±3.2%, respectively. However, results from static batch adsorption experiments have limited applicability to dynamic bacterialtransport in columns. The early breakthrough of E. coli relative to bromide was clear for all columns, namely c. 0.15 to 0.3 porevolume earlier. Column length had no significant effects on the E. coli peak concentration or on total recovery in leachate, indicatingretention in the top layer of sands. Tailing of breakthrough curves was more prominent for all fine sand columns than their coarse sandcounterparts. Bacterial recovery in leachate from coarse and saturated sand columns was significantly higher than from fine andunsaturated columns. Observed data were fitted by the convection–dispersion model, amended for one-site and two-site adsorption toparticles, and for air–water interface (AWI) adsorption. Among all models, the two-site+AWI model achieved consistently highmodel efficiency for all experiments. Thus it is evident from experimental and modeling results that AWI adsorption plays animportant role in E. coli transport in sand columns.© 2007 Elsevier B.V. All rights reserved.

Keywords: Bacterial transport; Escherichia coli; Sand moisture characteristics; Air water interface (AWI); Convection dispersion equation;Adsorption isotherms; One-site model; Two-site model

1. Introduction

Microbial transport in porous media is critical in avariety of fields, especially groundwater contaminationand bioremediation of groundwater or soil. Concerns

⁎ Corresponding author. Present address: Institute of Sustainability andInnovation, Victoria University (Werribee Campus), PO Box 14428,Melbourne, VIC, 8001, Australia. Tel.: +61 431 573 255; fax: +61 39919 8284.

E-mail addresses: [email protected],[email protected] (G. Jiang).

0169-7722/$ - see front matter © 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.jconhyd.2007.01.010

about bacterial transport in subsurface environmentshave been raised by intensified use of farm land as thedigestion medium for animal wastes (Gerba and Smith,2005). Animal or human septic wastes contain largenumbers of different microbes, such as faecal coliformsand streptococci, Giardia, Cryptosporidium, rotaviruses(Unc and Goss, 2004). The risk of groundwater con-tamination has promoted the study of microbial trans-port in porous media (Morris and Foster, 2000).

Bacterial fate and transport is affected by propertiesof the cells, the porous medium and the transporting

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http://dx.doi.org/10.1016/j.jconhyd.2007.01.010

3G. Jiang et al. / Journal of Contaminant Hydrology 93 (2007) 2–20

solution (Beven and Germann, 1982; Breitenbeck et al.,1988; Stenstrom, 1989; Gannon et al., 1991; Bitton andHarvey, 1992; Huysman and Verstraete, 1993b; Abu-Ashour et al., 1994; Hekman et al., 1994; McCaulou andBales, 1995; Weiss et al., 1995; Lindqvist andBengtsson, 1995; Natsch et al., 1996; Powelson andMills, 1998; Powelson and Mills, 1998; Fisk et al.,1999; Kim et al., 1999; Hendry et al., 1999; Jewett et al.,1999; Li and Logan, 1999; Aislabie et al., 2001; Bankset al., 2003; Becker et al., 2004).

Bacterial transport in soil involves both vadose andsaturated zone flow. Unsaturated transport is morecomplicated (Schafer et al., 1998) and has greaterpotential to remove bacteria (Lance and Gerba, 1984;Chu et al., 2001; Lenhart and Saiers, 2002; Chu et al.,2003). The main retention mechanisms are straining orfiltration by the solid matrix, and adsorption to air–water interfaces (AWI) or thin water films. (Tan et al.,1992; Wan et al., 1994; Wan and Tokunaga, 1997;Schafer et al., 1998). The AWI has high sorption af-finity for bacteria, usually considered irreversible, un-less microbes are re-mobilised into the flow by removalof AWI during increased wetting. Powelson and Mills(2001) found that unsaturated flow gave greaterbacterial removal than saturated flow. Transient flow(during heavy rainfall or flood irrigation) accompaniedby variable saturation might dissolve or mobilise airbubbles, releasing a higher percentage of bacteria intooutflow water.

Although AWI effects have been stressed in theliterature, column-scale experimental data are verylimited. Thus column measurements coupled withappropriate modeling of AWI effects can improve un-derstanding of the role of AWI in unsaturated bacterialtransport, relative to the roles of pore-size effects, andsolid–water adsorption. Therefore, it is hypothesizedthat strong bacterial adhesion to AWI should have apronounced effect on bacterial breakthrough in sandcolumns. This paper presents results for unsaturatedand saturated bacterial transport through coarse andfine sand columns, and investigates the effects onbacterial transport and retention of AWI correlatedwith water saturation, particle size, and column length.Mechanistic models were developed, with separateterms for bacterial attachment to solid–water and air–water interfaces, and evaluated using the experimentalresults.

Silica sand columns with tension infiltrometer andflow-controlled irrigation were used to produce bac-terial transport with various water saturations. Waterflow and matric potential in the sand columns werewell-controlled in order to establish a steady unsatu-

rated condition. The drainage water concentrations ofbacteria and an inert tracer (sodium bromide) werecontinuously-monitored. To model unsaturated bacte-rial transport, the traditional convection dispersionequation (CDE) model was modified by a first-orderkinetic term to account for AWI attachment, and termsfor the equilibrium/first-order adsorption to grain sur-faces. Also, we compared a one-site, first-order kineticmodel, and a two-site model with and without AWIeffects. Where possible, equilibrium and equilibrium+AWI models were also employed to fit experimentaldata.

2. Materials and methods

2.1. Sand treatment and characterization

The sands were Oamaru silica sands from FultonHogan Ltd., Christchurch, NZ, sieved into coarse(COSN500 m) and fine (FOSb500 μm) fractions.Trace organic matter and clay particles were removedusing a modification of the method of Chu et al.(2001), Kunze and Dixon (1986), and Powelson andMills (1998). The sand was soaked in 10% HNO3 for24 h, rinsed with deionised water, then soaked in0.5 mol/L NaOH for 2 h and rinsed with deionisedwater again.

The sand bulk density ρb and particle density ρpwere measured to calculate porosity, ε=1−ρb/ρp (Jianget al., 2005).

Sand moisture characteristics (the relationship be-tween volumetric water content θ and matric suction h,in kPa) were determined by a Buchner funnel connectedwith manometer tube and burette. Both water release(drying) and imbibing (wetting) processes were mea-sured. The observed data were fitted with the vanGenuchten (1980) model (Eq. (1)), and relative hy-draulic conductivity Kr(h) was calculated with the fittedparameters.

h ¼ hr þ ðhsat−hrÞ½1þ ðahÞn�m ð1Þ

KrðSÞ ¼ 1−ðahÞn−2½1þ ðahÞn�−m½1þ ðahÞn�2m : ð2Þ

Here θr= residual water content, θsat=saturated watercontent, α is equivalent to the inverse of air entry suction,n=1+λ, where λ is pore-size index, and m=1−1/n.

Because the unsaturated flow in our experiments wasdeveloped by gradually decreasing θ, only water releasecurves were used.

Fig. 1. Schematic diagram of sand column setup.

4 G. Jiang et al. / Journal of Contaminant Hydrology 93 (2007) 2–20

2.2. Bacteria and adsorption to sand

Escherichia coli strain D was used as the tracerbacterium. E. coli is a member of the family Enter-obacteriaceae of facultatively anaerobic, gram-negative,non-sporing rods, often motile organisms (Bell andKyriakides, 1998). Cell dimensions are about 2 μm longand 0.7 μm in diameter (Smith-Keary, 1988). A singlecolony was inoculated into nutrient broth (Life Tech-nologies, Paisley, Scotland) and incubated in a rotaryshaker (120 rpm) at 37 °C for 48 h. The final con-centration reached c. 1×109 cfu/mL, and the suspen-sion was stored in a fridge at 4 °C before use in theexperiments.

The method used to measure E. coli adsorption toboth sands, at concentrations of 104, 105, 106, and 107

cfu/mL, was modified from Ling et al. (2002) andHuysman and Verstraete (1993b). First, bacteria innutrient broth were centrifuged at 4000 rpm (HeraeusVarifuge 3.0R, rotor radius 21.1 cm) for 10 min and

washed twice with deionised water. The strains werere-suspended in deionised water and adjusted toconcentrations of 105 and 106 cfu mL− 1. Bacterialsuspension (20 mL) and 2 g sand were added to a50 mL conical tube, which was vortexed vigorously for10 s, and mixed for 1 h on a rotary shaker at 100 rpm.The tubes were then centrifuged at 120 ×g rcf(700 rpm) for 30 s. Cell concentration in the super-natant was determined by serial dilution and platecounting in Difco™ mFC agar (Becton, Dickinson andCompany, Sparks, USA). All tests were done in trip-licate. Bacterial adsorption was expressed as:

Kd ¼ Cs=Cw ð3Þ

Cs ¼ ðNt−NsÞ=W ð4Þ

Cw ¼ Ns=V ð5Þ

Pa ¼ ½ðNt−NsÞ=Nt� � 100: ð6Þ


Cs and Cw are cell concentrations on solid (cells g−1)and in liquid (cells mL− 1). Kd is a distributioncoefficient (mL g− 1). Nt and Ns are the total numberof bacteria added to the sand (cfu) and in the su-pernatant (cfu). W is the mass of sand (g), and V isthe volume of water in the mixture (mL). Pa is the %adsorption.

2.3. Sand columns

Fig. 1 is a schematic diagram of the sand column(45 cm long), which consisted of four or two PVC ringswith 22.5 cm inner diameter, joined by sealant andwaterproof tape. Two lengths of column, 45 cm and23 cm, were set up by using four or two rings. Thecolumn base had one layer of nylon mesh (60 μmpores), two layers of polyester mesh (23 μm pores,Universal Screen Supplies Ltd., NZ), and a stainlesssteel mesh. This combination was designed to sustainthe required suction up to 5 kPa without air bubblingthrough. A test of bacterial passage through the mem-brane and mesh showed no capture of E. coli at variousconcentrations (103–109 cfu/mL).

Fig. 2. The experimental setup for E. co

Tensiometers were installed at the middle of eachring with 20° downward angle. Honeywell 26PC pres-sure transducers were connected to a datalogger (CR10,Campbell Scientific Instruments) and calibrated. Thesuction of the tension infiltrometer and position of outletsiphon pipe were adjusted according to the tensiometerreadings.

2.4. Transport experiment

Fig. 2 shows the experimental system. To avoid edgeflow effects, sand columns were packed using a mo-dified method from Lenhart and Saiers (2002), designedto achieve standard packing, remove entrapped air andproduce full saturation (Jiang et al., 2005).

To diminish potential growth or die-off of E. coli,experiments were carried out in a dark room at 7±1 °C.As a coupling layer between the infiltrometer andcolumn, c. 10–15 mm of silica sand of grain size 75–297 μm was poured over a polyester cloth. For eachcolumn, the tension infiltrometer and hanging tube wereadjusted to control sand water suction, s. For unsaturat-ed flow, s was adjusted until the suctions stabilised.

li transport through sand columns.

Table 2Characteristics of Oamaru sands, COS and FOS-3

Sand COS FOS

Grain size (μm) 500–2360 75–500Specific surface area (cm2/g) 26.2 182.7Bulk density (g/cm3) 1.61 1.63Particle density (g/cm3) 2.67 2.65Porosity θ 0.39 0.38


Bacteria from the prepared suspension (Section 2.2)were re-suspended in deionised water and adjusted toan initial concentration of c. 108 cfu mL−1. Two sub-samples were collected to determine the initialconcentration. NaBr was added to the suspension toreach a concentration of 0.01 g mL−1. Once the columnequilibrated, the polyester cloth and contact sand wereremoved and the E. coli suspension was sprayed uni-formly over the sand surface by a sprayer. The cloth andsand were replaced, and the infiltrometer reinstalled.Leachate collection was about 1 L every sample, withtime interval ranging from 2 min for COS columns to30 min for FOS columns.

After each experiment, sand from the columns wasautoclaved at 121 °C to remove all the remainingbacteria. The column and infiltrometer were sterilizedwith 75% ethanol solution. Before starting new runs,initial leachate was checked to ensure no remainingbacteria from the last run. Parameters for the eight runsare listed in Table 1.

2.5. Sampling and assay

The leachate samples were collected in sterile 1 Lbottles or plastic bags, and stored at 7 °C for no morethan 12 h. E. coli concentration was measured bymembrane filtration, serial dilution and plate countingin mFC agar. Leachate samples were weighed for thecalculation of dimensionless transport time i.e. porevolume. Subsamples were collected for the analysisof bromide concentration, using a Dionex DX–120Ion Exchange Chromatograph fitted with a DionexAS 50 Autosampler and integrated by ChromeleonPeaknet 6.0.

Table 1Transport experiments and parameters

Run a Sand b Length(cm)

Suction(kPa)

Degree ofsaturation

E. coli C0

(cfu/mL)Flow rate(mL/min) c

CAO COS 44 0 1 4.15×108 776.9CB◊ COS 44 1.0 0.67 4.01×108 172.7CCO COS 22 0 1 9.25×108 255.8CD◊ COS 22 1.2 0.49 5.08×107 209.2FAO FOS 44 0.1 1 5.55×108 50.9FB◊ FOS 44 1.3 0.9996 3.74×108 63.0FCO FOS 22 0 1 4.80×108 146.7FD◊ FOS 22 1.5 0.9991 4.21×108 118.9

a Runs under saturated conditions are indicated by filled diamonds O;unsaturated runs by empty diamonds ◊.b COS and FOS = Coarse and Fine Oamaru Sand.c Flow rate was calculated based on sample weight and sampling

intervals.

2.6. Mathematical models

2.6.1. Equilibrium, one-site, and one-site+AWI kineticmodels

Mechanistic models of breakthrough curves (BTCs)can be used to identify key processes and their para-meters. Saturated, steady-flow transport has beenwidely modeled by the convection–dispersion equa-tion supplemented by a sink term accounting for at-tachment (Corapcioglu and Haridas, 1985; Harvey andGarabedian, 1991; Hornberger et al., 1992; Lindqvistet al., 1994; Tan et al., 1994; Lenhart and Saiers,2002).

AcAt

¼ DA2c

Az2−m

AcAz

−k1c ð7Þ

Here c is the bacterial concentration in soil solution(cfu/mL), D is the dispersion coefficient (cm2/min), v isthe pore water velocity (cm/min), k1 is an adsorptionrate constant (min−1), t is time (min) and z is distancefrom the inlet (cm). Let s be the attached bacteria atsolid–water interfaces (cfu/g). Assuming particle at-tachment is a rapidly equilibrating process, it can bedescribed as a linear isotherm.

s ¼ Kdc ð8Þ

AsAt

¼ KdAcAt

: ð9Þ

Kd is a distribution coefficient (mL/g). The other typeof reversible adsorption assumes a first-order kineticreaction for the rates of adsorption and desorption fromsolid–water interfaces.

qbhAsAt

¼ k1c−qbhk2s: ð10Þ

Here k2 is a desorption rate constant (min− 1).Eq. (10) is the nonequilibrium one-site kinetic model.

Fig. 3. Particle and pore-size distributions for the Oamaru sands.


Unsaturated transport requires another sink term torepresent adsorption at air–water interfaces (AWI).Due to strong bacterial affinity to AWI, this term canbe modeled as an irreversible adsorption (Schaferet al., 1998; Lewis et al., 2004).

AcAt

¼ DA2c

Az2−m

AcAz

−k1cþ k2qbhs−k0c: ð11Þ

k0 is the AWI sorption rate constant (min−1). Thus,the equation for 1-D bacterial transport in homoge-

neous, unsaturated porous media with first-order kineticadsorption (or filtration) is

AcAt

þ qbhAsAt

¼ DA2c

Az2−m

AcAz

−k0c: ð12Þ

This one-site AWI model can be applied to experi-mental data by STANMOD 2.2 (US Salinity Laborato-ry), by assuming k0 to be μ (the first-order decaycoefficient) and f=0 (fraction of adsorption sitesat equilibrium assumed to be zero) in the two-site

Fig. 4. Moisture characteristics for coarse and fine Oamaru sand (COS and FOS), including the fitted functions shown in Eqs. (1) and (2) above.


nonequilibrium model (Toride et al., 1999). For the inerttracer (bromide), BTCs were fitted by the equilibriummodel assuming no adsorption to sand particles.

2.6.2. Two-site, and two-site+AWI kinetic modelsFuller et al. (2000) reported that bacteria in intact

soil experiments had a range of cell subpopulations,which might have different adsorption affinities. Also,sand particle surfaces might not be homogenous forbacterial adsorption. Thus it is better to include bothequilibrium and kinetic bacterial adsorption in theCDE model, and a two-site model including bothprocesses was developed. AWI attachment was stillmodeled as a first-order irreversible adsorption.

AcAt

þ qbh

As1At

þ As2At

� �¼ D

A2c

Az2−m

AcAz

−k0c: ð13Þ

Here s1 and s2 are bacteria attached on media sur-faces by equilibrium or kinetic adsorption, respectively(cfu/g).

As1At

¼ f dKdAcAt

ð14Þ

As2At

¼ ð1−f Þ hqb

k1c−k2s2: ð15Þ

f and (1− f) are respectively the fractions of exchangesites assumed to have equilibrium and kinetic adsorp-tion. This two-site model can again be fitted bySTANMOD 2.2 by assuming k0 to be the first-orderdecay coefficient. The attachment coefficients arecalculated as follows.

k1 ¼ aðR−1Þk2 ¼ a:

ð16Þ

α is the first-order kinetic coefficient in CXTFIT,and R is the retardation factor. Results from the one-site and one-site+AWI models were compared withthe two-site models. Also, equilibrium and equilibri-um+AWI models were used if STANMOD suggestedtheir potential applicability.

2.6.3. Model evaluation and sensitivity analysisThe efficiency of a model fit to data is usual-

ly described by the coefficient of determination,E (Hornberger et al., 1992; Reddy and R.M.F., 1996;Hendry et al., 1999), defined as

E ¼ 1−

XNi¼1

ðCifit−C

iobsÞ2

PNi¼1

ðCifit−CavgÞ2

ð17Þ

Cavg ¼XNi¼1

Ciobs=N : ð18Þ

Cfiti and Cobs

i are the fitted and observed bacterialconcentrations (N values) at time ti, and Cavg is theobserved mean value. However this method givesgreater weight to larger C values, so E was com-bined with visual inspection to evaluate the goodnessof fit.

To examine the effects on the predicted break-through curves of the three fitting parameters (k1, k2,and k0) in the one-site kinetic CDE model (or fourparameters k1, k2, k0, and f in the two-site model),each parameter was varied while others were keptconstant. The range of variation was selected to coverpossible variation in our experiments. In CXTFIT 2.1,

Fig. 5. Distribution of degree of saturation in COS sand columns. Error bars are standard deviations for saturation variance during the whole leachingevent.


the dimensionless input parameters β and ϖ for theone-site+AWI model were calculated as:

b ¼ 1R

ð19Þ

ϖ ¼ k2ðR−1Þ Lm : ð20Þ

β is a partitioning variable; and ϖ is a mass transfercoefficient. For the two-site+AWI model, the equationswere:

b ¼ 1Rþ f 1−

1R

� �ð21Þ

ϖ ¼ k2RLmð1−bÞ: ð22Þ

To predict leachate bacterial concentration, weassumed a pulse injection of bacteria at the column

Fig. 6. Distribution of degree of saturation in FOS sand columns. Error bars aevent.

top with duration 0.2 pore volume; sand column lengthwas 40 cm; water flow rate was 10 cmmin−1; and D was3 cm2 min−1.

3. Results

3.1. Characteristics of Oamaru sand

Table 2 shows sand properties. Though both havevery similar porosity and bulk density, their specificsurface areas differed by a factor of about seven.

Fig. 3 shows particle and pore-size distributions.The pores were mainly 200 to 600 μm for COS; and30 to 60 μm for FOS (about one log of magnitudesmaller).

Fig. 4 shows themoisture characteristics. For COS, bothθ and Kr (Eqs. (1) and (2)) decreased abruptly from 0 to1 kPa, while for FOS they changed little from 0 to 2 kPa.

re standard deviation for saturation variance during the whole leaching


Thiswas consistent with our experimental observations: forFOS, different suctions sometimes gave no big differenceof water flow rate.

The fitted parameters were used to calculate watercontent and saturation in the experiment from suctionsmeasured by tensiometers.

3.2. Saturation, and matric suction

Figs. 5 and 6 show the degree of saturation versuscolumn depth. For saturated COS experiments, both44 and 22 cm columns had uniform full saturation.However, unsaturated experiments CB◊ and CD◊ didnot have uniform saturation. CB◊ had a large variation,from 0.53 to 0.8 at four depths, while CD◊ had values0.3 and 0.77 at top and bottom. This resulted from theCOS moisture release characteristic (Fig. 4). However,

Fig. 7. Matric suctions for COS experiments CA O, CB◊ and FOS experiments10 min intervals.

the suctions at different depths and water flow ratesduring runs were stable. The average saturation and flowrate were used in the models.

For all FOS experiments, saturation was uniform(Fig. 6). FB◊ and FD◊ applied only 1.3 and 1.5 kPa suc-tion, in the insensitive plateau range in Fig. 4. Un-saturated experiment FB◊ had a similar flow rate tosaturated FAO (Table 1).

Fig. 7 shows the variations of suction for experimentsCAO, CB◊, FAO, and FB◊. COS experiments alwaysshowed greater variation for both saturated and un-saturated columns, probably due to larger pore-sizes andflow rates, and because the water phase could changemore rapidly with small changes of environmental fac-tors. FOS experiments had better uniformity. Readingsfor tensiometers 1 (top) and 4 (bottom) coincide forFAO and FB◊, close to the suctions imposed by the

FA O, FB◊. Error bars are standard deviations of measured suctions over


infiltrometer and hanging tube. The variable nature ofsuction values indicates a slight nonuniformity of col-umn packing.

3.3. E. coli adsorption to Oamaru sand

The adsorption of E. coli onto sands was fitted bytwo isotherms (Fig. 8):

Langmuir Cs ¼ SmaxKLCw

1þ KLCwð23Þ

Freundlich Cs ¼ KdCmw : ð24Þ

Cs and Cw are the concentrations at equilibrium ofadsorbed bacteria (cfu/g) and in supernatant (cfu/mL).Smax is the maximum adsorption sites per gram of sand(cfu/g); KL is a constant related to binding energy (mL/cfu); Kd is a distribution (partition) coefficient (mL/g).The fitted Smax for COS and FOS were 1.45×1011 and8.58×1012 cfu/g, These are not proportional to thespecific surface areas of 26.16 and 182.71 cm2/g,suggesting that adsorption was not a pure surface in-teraction. The ratio of bacterial to particle sizes mayalso have contributed to adsorption. According to fit-ted values of KL, the affinity of E. coli to FOS washigher than COS, namely 6.65×10− 11 vs. 3.67×10− 11 mL/cfu.

The Freundlich parameter Kd for FOS was about 103

times higher than for COS, while the exponents m werenot significantly different, i.e. 1.3 and 1.37. Fig. 8 showsthat E. coli sorption (Pa) to Oamaru sand was not cor-

Fig. 8. Adsorption of E. coli to COS and FOS, fitted by Langmuir and Freundlfor Cw and Cs.

related with the cell concentration from 104 to108 cfu/mL. The average Pa for COS and FOS was 45.9±7.8%and 96.9±3.2%, respectively. Pa values were also notproportional to the specific surface area.

3.4. Model sensitivity analysis

A sensitivity analysis of the kinetic one-site+AWImodel (Eq. (11)) for parameters k1, k2 and k0 (Figs. 9and 10) showed that the BTC peak was lowered byincreases in k1 and k0, but with almost no influence onthe peak timing or on the BTC tails. Thus as k1 (ameasure of bacterial affinity to surfaces) increases, bac-terial leaching will decrease. Increase in k2 raised theBTC peak, and caused the BTC tails to move forwardand disappear rapidly. Thus the BTC peak is deter-mined by all three parameters, while the tail is influ-enced significantly by k2.

For the two-site+AWI model, Fig. 11 shows effectsof four key model parameters. As for the one-site+AWImodel, k0 only affects the peak concentration. However,the BTC peak timing was dramatically affected by k1,k2, and f. Increasing k1 and f delayed the BTC peak, andgave lower peak concentrations, while increasing k2 hadthe opposite effect. Also, k1 had a strong effect on theBTC tails in the two-site model, similar to the effect ofk2 in the one-site model.

3.5. E. coli breakthrough curves and modeling results

Table 3 shows recovery of bacteria and bromide aftercollecting c. 25 L of leachate. For the COS experiments,

ich isotherms. Horizontal and vertical error bars are standard deviations

Fig. 9. Effect of key model parameters (k1 and k2) on bacterial transport in sand columns, simulated by one-site+AWI model.


saturated runs CAO and CCO recovered over 80% ofapplied bacteria, almost double the recovery of un-saturated runs CB◊ and CD◊. Recovery in the FOS longcolumn experiments (FAO and FB◊) did not show thesame pattern. The saturated long column (44 cm) FAO

recovered ten times less bacteria than unsaturated FB◊.This is expected, considering that FAO had lower flowrate than FB◊ (Table 1). FCO (22 cm short column)recovered 17.5% of injected bacteria, more than tentimes higher than unsaturated FD◊. Column lengthshowed no significant effects on E. coli recovery in theCOS experiments. However, short columns with FOSrecovered significantly more bacteria (1–2 log of mag-nitude) than long columns. Bromide recovery reached

Fig. 10. Effect of model parameter k0 on bacterial transport

about 70% for both COS and FOS columns, withslightly greater recovery in saturated columns.

Leachate bacterial concentration of CAO was twicethat of CB◊, attributable to unsaturated flow of CB◊.CCO and CD◊ showed the same type of concentrationratio. However, the peak E. coli concentration for shortcolumns CCO and CD◊ reached only half of CAO andCB◊. Considering the variance of E. coli enumeration,this difference was probably not significant. The excep-tional lower leached concentration of FAO comparedto FB◊ was caused by the lower flow rate of FAO (seeTable 1). The concentration ratio between FCO and FD◊

was about 30, indicating the strong effect of watersaturation on E. coli transport through fine sands.

in sand columns, simulated by one-site+AWI model.

Fig. 11. Effect of model parameters k1, k2, k0, and f on bacterial transport in sand columns, simulated by two-site+AWI model.


Figs. 12 and 13 show fitted BTCs. For both COS andFOS, degree of saturation did not cause significant de-lay of E. coli breakthrough, except for CD◊ (Fig. 12).Powelson and Mills (1998) also observed that unsatu-rated conditions delayed E. coli breakthrough. Whileair–water interfaces and film straining can reducebacterial transport, change of water flow rate and sizesof water-filled pores would not produce strong effects onbreakthrough timing (in terms of normalized time, PV).Similarly, column length did not show any relationshipto the relative timing of breakthrough. There was also nosignificant difference of the peak timing of bacterialbreakthrough between COS and FOS experiments.

Table 3Recovery of bacteria and bromide after about 25 L of leachate collected

Run▴ CAO CB◊ CCO

Leachate volume (L) 25.64 25.90 28.14E. coli recovery (%) 89.00 54.36 80.71Bromide recovery (%) 74.34 71.45 73.03

▴Saturated and unsaturated sand columns are indicated by filled O and empt

The Peclet number (Pe=vL/D, the ratio of advectionto dispersion processes) for E. coli transport was cal-culated from estimated values of D and measured waterflow velocity (see Table 4). For all COS columns, therelative effect of advection increased when the AWIeffect was added into the model. However, this changewas not observed in fine sand columns FA, FC, and FD,probably due to the slower velocity and weaker advec-tion, and because less de-saturation created fewer AWIs.Nevertheless, advection is a much more dominant pro-cess (i.e. higher Pe) in fine than in coarse sand columns.This is supported by low values of D in FOS columns,suggesting both advection and dispersion decreased

CD◊ FAO FB◊ FCO FD◊

27.82 26.96 27.73 25.23 25.3344.25 0.002 0.03 17.45 1.4269.01 66.64 67.98 73.31 73.09

y diamonds ◊, respectively.

Fig. 12. Bromide and E. coli breakthrough curves for COS experiments (CA O, CB◊, CC O, and CD◊), fitted by equilibrium, one-site, and two-site+AWI kinetic models. Saturated and unsaturated columns are labeled by bold and italic fonts, respectively.


with slower water flow velocity. Table 4 also showshigher k0 values and hence AWI attachment rate forFOS than COS. k0 is regarded as proportional to AWIarea, which for saturations N0.5 was assumed to beproportional to air content (1−θ) (Schafer et al., 1998;

Lewis et al., 2004). Our estimated values of k0 werenot proportional to column air content, suggesting thatE. coli deposition at AWI might also depend on thecontact time and spatial dispersion of AWI. Slower flowrate in FOS provides more contact time and opportunity

Fig. 13. Bromide and E. coli breakthrough curves for FOS experiments (FA O, FB◊, FCO, and FD◊), fitted by equilibrium, one-site, and two-site+AWIkinetic models. Saturated and unsaturated columns are labeled by bold and italic fonts, respectively.


for AWI adsorption. Also, for the same air content, airbubbles in FOS were smaller than in COS, and AWIswere more stable in FOS than COS columns.

The saturated sand could have trapped air bubbles,forming AWIs contributing to E. coli retention. Table 4

indicates that the ratio of saturated to unsaturated k0values was about 1/2 (FCO/FD◊), 1/3 (CAO/CB◊), and 1/5 (CCO/CD◊). Exceptionally, no significant differencewas observed for k0 between FAO and FB◊, suggestingthat saturated FAO had more AWIs than unsaturated

Table 4Estimates of parameters obtained from one-site and one-site+AWI kinetic models a

Run b Model k0 k1 k2 k1/k2 D R Pe E

CAO OS 11.8 155 0.08 3.7 1.1 62 0.9324OSA 0.21 11.8 241 0.05 1l.7 1.1 133 0.9827

CB◊ OS 3.9 217 0.02 8.9 1.0 8.5 0.5817OSA 0.62 3.9 391 0.01 5.3 1.0 14.3 0.8677

CCO OS 7.8 26.7 0.29 1.2 1.3 31.8 0.9317OSA 0.23 7.8 30.1 0.26 0.7 1.3 51.8 0.9916

CD◊ OS 13.1 0.7 20.1 126 21.2 0.5 0.5323OSA 1.04 13.1 5.0 2.6 7.8 3.6 8.1 0.9496

FAO OS 0.1 0 164 0.1 165 131 0.0195OSA 11.5 0.03 0.01 2.2 0.1 3.2 112 0.7574

FB◊ OS 0.1 0 174 0.3 175 59.7 0.5282OSA 8.85 0.02 0.01 1.8 0.2 2.8 78.5 0.9260

FCO OS 0.15 0.01 24 0.2 25 139 0.0074OSA 2.16 4.3 5.9 0.7 0.9 1.7 24.4 0.9556

FD◊ OS 0.2 0 79.4 0.2 80.4 113 0.0841OSA 4.77 0.1 0.08 1.24 0.2 2.2 77.8 0.7386

a The first row represents estimates of one-site kinetic model (OS), the second row is estimates of one-site+AWI model (OSA).b Saturated and unsaturated sand columns are indicated by filled and empty diamonds, respectively.


FB◊, possibly due to trapped air bubbles during columnpacking.

Including AWI adsorption in the two-site model, fincreased at least ten times for all columns except CAO.AWI adsorption is relatively irreversible and thus bettermodeled by a first-order kinetic process. In the two-sitemodel, the AWI part of E. coli retention was accountedfor by adsorption at particle surfaces. Also, the two-sitemodel resulted in extremely large R, e.g. 575 and 321for FAO and FB◊ respectively. The AWI amended two-site model fitted the data and gave more reasonable R

Table 5Estimated model coefficients from the two-site and two-site+AWI models a

Run b Model K0 k1 k2 k1/k2

CAO TS 0.05 0.25 0.2TSA 0.14 0.08 0.9 0.09

CB◊ TS 0.02 0.00 8.9TSA 0.58 3.9 391 0.01

CCO TS 0.02 0.0 6.7TSA 0.23 7.8 30.8 0.25

CD◊ TS 0.14 0.0 67.3TSA 4.77 0.1 0.04 2.8

FAO TS 0.33 0.0 574TSA 21.9 0.05 0.01 9.7

FB◊ TS 0.16 0.0 320TSA 9.58 0.02 0.01 2.9

FCO TS 0.1 0.0 65.7TSA 1.78 0.02 0.01 1.9

FD◊ TS 0.26 0.0 176TSA 5.95 0.1 0.01 20.5

a The first row of data was estimates from two-site model (TS); the seconb Saturated and unsaturated columns are indicated by O and ◊, respectively

values (Table 5), consistent with the one-site modelresults.

The one-site kinetic model fitted bacterial BTCs insaturated COS columns as well as the one-site+AWImodel, but its fitting capability decreased for unsaturat-ed COS columns. The one-site model failed to fit data inboth saturated and unsaturated FOS columns. By con-trast, the one-site+AWI model fitted E. coli BTC data inall columns (saturated and unsaturated, COS and FOS).Also, the descending limbs and tailing of BTCs werefitted better by the one-site+AWI model than by the

Kd f D R Pe E

0.04 0.13 1.3 1.2 179 0.99010.02 0 1.7 1.1 242 0.99871.4 0 5.4 9.9 13.9 0.87060.002 0.56 5.7 1.0 13.3 0.87221.6 0.04 0.6 7.7 60.4 0.99710.06 0.9 0.6 1.3 59.1 0.99697.7 0.03 6.3 68.3 10.1 0.97750.3 0.6 3.2 3.8 19.5 0.9986

137 0.01 3.4 575 4.3 0.83942.3 0.3 1.6 10.7 9.2 0.871676.3 0.005 1.1 321 16.3 0.85620.7 0.09 0.5 3.9 38.1 0.930015.7 0.01 0.8 66.7 26.0 0.99170.5 0.3 0.6 2.9 33.1 0.999042.0 0.01 1.7 177 10.2 0.94704.9 0.1 2.9 21.5 5.9 0.9748

d row was estimates from two-site+AWI models (TSA)..

Table 6Estimated model coefficients from the adsorption equilibrium (EQ)and equilibrium+AWI models (EQ+AWI)

Run a Model D R Kd k0 E

CAO EQ 2.6 1.06 0.02 – 0.9516EQ+AWI 1.7 1.05 0.01 0.2 0.9829

CB◊ EQ 8.9 1.02 0.003 – 0.5817EQ+AWI 4.9 1.0 0.6 0.0002 0.8737

CCO EQ 0.9 1.27 0.06 – 0.9518EQ+AWI 0.6 1.25 0.06 0.2 0.9969

CD◊ EQ 1190 180.5 20.5 – 0.6608EQ+AWI 7.4 3.5 0.29 1.0 0.9572

FCO EQ+AWI 0.9 1.7 0.18 2.2 0.9582

a Saturated and unsaturated columns are indicated by O and ◊,respectively.


one-site model. The two-site and two-site+AWI modelsfitted well for all columns. Also, the equilibrium+AWImodel fitted well to coarse sand experiments. Tables 4,5, and 6 show that the two-site+AWI model gave thebest fit.

4. Discussion

4.1. Experimental design

Our previous experiment (Jiang et al., 2005) used atension infiltrometer placed on an undisturbed field soilcore (lysimeter) to control suction at the top, and simu-lated irrigation effects on E. coli transport. The top layerwas dried out more than the deeper layer, as occursfrequently in pastures. Pore heterogeneity made it diffi-cult to determine at what depth bacteria were removed.Only net removal could be studied, by measuring per-colate bacterial numbers. Once bacteria were applied tothe lysimeter, residual cells prevented its re-use with thesame bacteria, if no effective method of sterilization wasapplied without destruction of the soil structure. Oneway to circumvent this problem is to use bacteria withminor differences in properties, such as resistance todifferent antibiotics.

To enable a more controlled study of E. coli move-ment, a new experimental approach was designed in thispaper. Stable suctions and uniform pore-size distributionthroughout the whole column were needed. Also, it wasnecessary to sterilize the system (kill residual bacteria)without changing the medium properties. This requiredsystem design so that effects of macropores (N30 μm)compared to micropores on bacterial movement couldbe studied by changing the suction.

Experiments reported in the literature usually haveused a hanging tube to control suction at the columnbase (Schafer et al., 1998; Jewett et al., 1999; Saiers and

Lenhart, 2003). However, the hanging tube may causesome post-leaching mixing and dispersion in the siphon.While our previous experiment (Jiang et al., 2005) useda tension infiltrometer at the lysimeter top, this experi-ment used that along with a hanging tube at the bottom.The virtues of this setup are well-controlled water flowand suctions, continuously-monitored water potentialand infiltration rate, and the capability for reproducibletests. However, shortcomings of the system include thefollowing. The inserted tensiometers might interfere withthe water flow in sands. Even with uniform packing, thesands did not reach uniform water content versus depth.Also, it was found that autoclaving sands at 121 °C pos-sibly reduced sand particle sizes. It is recommended thatnew sands are used between tests. The packing procedurealso strongly influenced hydraulic conductivity and there-fore E. coli transport, so a constant packing proceduremust be used.

4.2. E. coli adsorption

The adsorption of E. coli strain D to both sandsshowed that both the % adsorption, Pa, and fittedmaximum adsorption sites per gram (Smax) were notcorrelated with specific surface area. Smax values for FOSwere about 10 times those for COS, suggesting thatE. coliadsorption is not linearly related to the contact surfaces.(Bengtsson and Ekere, 2001) reported that the sorptioncoefficient of bacteria to soil particles (Kd) measured bybatch experiments and that predicted by transport modelswere contrary, with one being directly proportional to thespecific surface area and one not. Also, our batchexperiments showed that particle size is an importantdeterminant of adsorption coefficient, inconsistent withthe findings of Bengtsson and Ekere (2001). The relativeimportance of cell surface hydrophobicity and sandsurface charge might explain this discrepancy. We alsopostulated that E. coli adsorption is not just related tospecific surface area, but also to different interactionsbetween cells and particle surfaces (e.g. abrasion of solidphase surfaces, compression of the hydrodynamicboundary layer, and degree of suspension of the particles).Thus the applicability of results from static adsorptionexperiments to dynamic bacterial transport is limited.

Huysman and Verstraete (1993a) reported that bac-terial adsorption to soil reached equilibrium swiftly, in15 to 20 min. Batch adsorption conditions are quitedifferent from those in flowing columns. However, themeasured Pa can be used to estimate bacterial recoveryin leachate. The Pa for COS was 45.9%, and E. coli re-covery for CB◊ and CD◊ was 54.4% and 44.3% respec-tively, very close to the percentage of suspended (non-


adsorbed) bacteria (i.e. 100–45.9=54.1%). However,CAO and CCO recovered much higher percentages, indi-cating that many bacteria reached the outlet pipe beforereaching equilibrium adsorption. FCO and FD◊ showedthe same trend as coarse sand columns, though FAO andFB◊ were exceptions because of their flow rates and hy-draulic properties of fine sand (see Sections 3.5 and 3.1).

E. coli retention was mainly attributed to adsorption toparticle surfaces and air–water interfaces. For fine sands,lower hydrodynamic shearing forces should cause lessdetachment. Similarly, adsorption to AWI increased withmore unsaturated conditions (greater air–water interfacearea). Thus unsaturated fine sand columns retained morebacteria than coarse sand columns, due to: slower waterflow, greater particle surface area, and more dispersedair–water interfaces.

4.3. BTCs

Table 4 shows bromide recovery was not correlatedwith column length, sand particle sizes, or water con-tent. Thus bromide was a good inert tracer. Its leachateconcentration can provide key transport parameters, in-cluding water flow rate and dispersion coefficient. Theearly breakthrough of bacteria relative to conservativechemical tracers (e.g. bromide), due to pore-size exclu-sion, is frequently reported (Sinton et al., 2000). It wasevident for coarse sand columns, for both saturated andunsaturated flows (Fig. 12), with lag times of 0.15, 0.11,0.3, 0.3 pore volume for CAO, CB◊, CCO, and CD◊

respectively; and also for fine sand columns, except forFAO (Fig. 13). Early breakthrough in fine sand columnswas also discriminable: for FAO it can be observedclearly from the simulated BTCs, although the measureddata points do not indicate it. E. coli concentrationspeaked c. 0.15, 0.3, 0.15 pore volume earlier than bro-mide for FB◊, FCO and FD◊ respectively.

For coarse sand columns, the ratio of E. coli peakconcentrations for saturated and unsaturated columnswith the same length was c. 5 (CAO/CB◊=5.5, CCO/CD◊=5). The ratios for FAO/FB◊ and FCO/FD◊ were0.04 and 25.5, respectively. This suggests strongerbacterial retention in fine sands, presumably becausethey had higher adsorption affinity and smaller poreswith slower flow.

Column length had no clear effect on E. coli peakconcentration. (Jiang et al., 2005) reported that Bacillussubtilis applied to a soil lysimeter concentrated in thetop 10 cm. Therefore, the differences between 44 and22 cm columns were mainly due to different water flowrates. Peak leachate E. coli concentration for fine sandcolumns was at least ten times lower than for coarse

sand columns. Thus, particle size significantly affectedE. coli transport and retention.

BTC tailing was observed in fine sand columns(Fig. 13), caused mainly by E. coli adsorption andsubsequent desorption from grain surfaces. Anotherexplanation is that some bacteria were stored in porewater, then gradually replaced by flowing water.

4.4. Effects of matric suction

The largest water-filled pore diameters for theapplied suctions of 1, 1.2, 1.3 and 1.5 kPa are 300,250, 230 and 200 μm. Bacteria applied to sand columnswith different suctions thus entered different sized pores.Sirivithayapakorn and Keller (2003) reported that thesmallest ratio of pore to cell size was 1.5 for bacteriaentering a pore. Both COS and FOS have pores wellabove this threshold value (Fig. 3). Thus small poreentrapment was not significant in our experiments.

The volume flow rate in a pore is proportional to thefourth power of its equivalent diameter. Thus, mostflow is through bigger pores. The difficulty of dis-placing water in small pores, following their storage ofapplied bacteria, may help explain the greater retentionin FOS than COS, and in unsaturated than saturatedcolumns. Thus, it is arguable that matric suction, alongwith water content, provide the best indicators of bac-terial retention.

4.5. Modeling approaches

Bacteria will tend not to enter smaller, slower-flowpores, thus irreversible attachment must have occurred,considering the low bacterial recovery, even after sub-stantial volume of leachate (Sirivithayapakorn andKeller, 2003). In unsaturated sand, this irreversibleattachment was mainly due to adsorption to AWI andpartly to sand grains. Thus, mechanistic models weredeveloped by incorporating adsorption terms into thetraditional CDE model to simulate E. coli transport insaturated/unsaturated media.

Bacterial adsorption can be modeled as an equilib-rium or a kinetic term in CDE models (Corapcioglu andHaridas, 1984; Harvey and Garabedian, 1991; Lindqvistet al., 1994; Reddy and R.M.F., 1996). Equilibriumadsorption usually assumes almost instantaneous equi-librium. A one-site kinetic process could approachequilibrium if the rates of attachment and detachmentare the same. Considering only total E. coli recoveryin leachate, discrimination between equilibrium and ki-netic adsorption may not be critical. Also, in natu-ral systems, bacterial concentrations are relatively low


compared with adsorption sites available in subsurfaces,so there is no need to include blocking or ripeningeffects in the sand column models (Schafer et al., 1998).

However, for monitoring E. coli concentrations inleachate and predicting E. coli spreading in large-scale,more complex field applications, more comprehensivetwo-site and two-site+AWI models are proposed to bemore appropriate. The proposed models can cope withdifferent media (coarse and fine in our experiments), andwith variable water content or flow rate. The two-site+AWI model clearly had the greatest model efficiencyamong all models examined here. However, f in thetwo-site model could not be physically measured andthe fitted value might just be an artifact. Although thisis an intrinsic flaw, it is still a simple and effectivemodeling approach.

5. Conclusion

A sand column system was built to investigate theeffects of water saturation, particle size, and columnlength on leaching of E. coli. Special virtues of the setupwere: well-controlled water suctions and flow; contin-uously-monitored water potential and infiltration rate;and hence capability for reproducible tests. Percent ad-sorption (Pa) of E. coli strain D to silica sands wasmeasured in batch experiments, and was not correlatedwith the initial cell concentration. The applicability ofstatic batch adsorption data to dynamic transport experi-ments is limited.

Breakthrough curves were fitted by the convection–dispersion kinetic model, modified by added one-site ortwo-site and AWI adsorption terms. Early breakthroughof E. coli relative to bromide was clear for all experi-ments. Column length had no evident effects on theE. coli peak concentration, consistent with our previousobservation that bacteria were retained in the c. top10 cm of a soil (Jiang et al., 2005). Due to strongerE. coli adsorption in fine sand (FOS), BTC tailing wasprominent for all fine sand columns. E. coli recovery inleachate from coarse sand (COS) was significantlyhigher than for FOS columns. Thus, particle size sig-nificantly influenced bacterial transport and retention.Saturated columns also yielded more leached bacteriathan corresponding unsaturated columns.

Equilibrium, one-site, two-site and their AWI ad-sorption amended models were fitted to data. The two-site+AWI model achieved high model efficiencyfor all experiments. Although it had some intrinsicflaws, its goodness of fit, simplicity, and well-es-tablished mechanisms supported it as the most ap-plicable model.

Acknowledgements

This research was supported by the New ZealandFoundation for Research, Science and Technology(FoRST). The authors acknowledge technical supportfrom the Soil and Physical Science Group, especiallyJason Breitmeyer and Joy Jiao for bromide analyses.G. Jiang is grateful for a postgraduate scholarship fromNZAID (NZ Agency for International Development).

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