Title: Evaluating E. coli transport risk in soil using dye and bromide
tracers
Fiona P. Brennana,b, Gaelene Kramersa,c, Jim Grantd, Vincent O’Flahertyb,
Nicholas M. Holdenc and Karl Richardsa*
aTeagasc,
Environmental Research Centre, Johnstown Castle, Wexford,
Ireland
bMicrobial
Ecology Laboratory, Microbiology, School of Natural Sciences and
Ryan Institute, National University of Ireland, Galway, Ireland
cUCD
Bioresources Research Centre / Biosystems Engineering, University
College Dublin, Ireland
dTeagasc,
Ashtown Research Centre, Dublin, Ireland
Email addresses: Fiona.brennan@teagasc.ie (F. Brennan),
gkramers@bluebottle.com (G. Kramers), Jim.grant@teagasc.ie (J. Grant),
vincent.oflaherty@nuigalway.ie (V. O’Flaherty), nick.holden@ucd.ie (N.
Holden), Karl.richards@teagasc.ie (K. Richards)
* Corresponding Author. Mailing Address: Teagasc, Environmental Research
Centre, Johnstown Castle, Wexford, Ireland. Phone +353539171261. Fax
+353539142213.
E-mail Karl.richards@teagasc.ie.
Acknowledgements
This work was funded by the Irish research council for Science, Engineering
and Technology (IRCSET) and supported by Teagasc, Johnstown Castle. The
authors wish to thank the farm and research staff at Johnstown Castle,
Grange and Moorepark research centres for assistance with and
accommodation of field trials, and Dr Cormac Murphy (University College
Dublin) for the provision of the E. coli isolate.
Title: Evaluating E. coli transport risk in soil using dye and bromide
tracers
Abstract
Dye and bromide tracers are established methods of assessing the presence,
function and extent of hydrological pathways in soil. Prediction of E. coli
transport pathways in soil, using brilliant blue (BB) dye and bromide tracers,
was investigated using in-situ field trials on three grassland soil types, under
different moisture regimes. Passive transport through preferential flow routes
was the dominant mechanism of vertical E. coli transport in the soils studied.
However, lateral movement of E. coli from macropores to the soil matrix was
also observed. E. coli transport was mainly associated with visualised
infiltration patterns but there was some evidence of differential transport of BB
and E. coli. Maximum E. coli depth was found not to co-occur with BB and
bromide tracers in 44 and 71% of samples, respectively. Soil type and
season of application were important in the distribution and maximum depth of
E. coli, and the relationship between the bacterium and its tracers. Moisture
content was found to be important for the relationship between E. coli and BB,
and the extent of this effect varied with soil type. There was a trend of
increasing E. coli concentrations to a peak sample moisture concentration of
0.3 to 0.4 g g-1 dry soil followed by a decrease. Overall BB was found to have
greater predictive value than Br. Correlation and co-occurrence analysis found
that shortly after land application both BB and Br were good predictors of E.
coli transport pathways and distribution under certain conditions, but
underestimate risk to shallow groundwater.
Abbreviations: BB, brilliant Blue; TSB, tryptone Soya Broth; PBS, phosphate
buffered saline; JC, Johnstown Castle; G, Grange; M, Moorepark; CFU,
colony forming units; MPN, most probable number; WRB, world reference
base; OM, organic matter.
Introduction
The contamination of groundwater, including potable water supplies, with the
causative agents of disease continues to be of worldwide concern (World
Health Organisation, 2006). Even within developed countries, where water
and wastewater treatment infrastructure has been improved considerably in
recent years, outbreaks of waterborne diseases frequently occur (Karanis et
al., 2007; O'Reilly et al., 2007; Strachan et al., 2001; Unc and Goss, 2006).
Remediation of contaminated groundwater is extremely difficult, and
enteropathogens can survive for significant periods posing a prolonged risk to
consumers of contaminated drinking water supplies (John and Rose, 2005).
While a variety of sources of contamination have been identified, international
evidence suggests that the landspreading of animal slurries, manures and
soiled waters are significant contributors (Chadwick et al., 2008; Fenlon et al.,
2000; Gerba and Smith, 2005). A large array of enteric pathogenic bacteria,
protozoa and viruses may be present in agricultural materials potentially
resulting in diffuse pollution when released into the environment by
landspreading (Duffy, 2003; Hutchison et al., 2004; Vernozy-Rozand et al.,
2002).
Soils are relied upon to mitigate the biological risk factors of
landspreading by both retention and attenuation, and they are often perceived
as a barrier for the protection of groundwater from landspread
enteropathogens (Tim et al., 1988). However, observed leaching of microbial
enteropathogens through soil (Brennan et al., 2010; Jackson et al., 1998;
Powelson and Gerba, 1995) indicates that soils cannot always prevent
enteropathogen transport to groundwater (Gagliardi and Karns, 2000; Unc
and Goss, 2004). As such, an understanding of the factors affecting the
transport of pathogens through the unsaturated zone of soil/subsoil (vadose
zone) has become essential for the evaluation of the risk to groundwater
posed by landspreading of biosolids and animal manures, on-site waste water
treatment plants (Gill et al., 2007), and the development of mitigation
strategies.
Preferential flow routes are increasingly being viewed as ‘super
highways’ for enteropathogen transport in the vadose zone (Abu-Ashour et
al., 1994; Artz et al., 2005; Darnault et al., 2003; McLeod et al., 2008). Smith
(1985) considered that the degree of macropore flow determined the extent of
E. coli transport in soils. Preferential flow occurs when infiltrating water from
the surface bypasses the soil matrix, through pathways in the soil profile,
potentially causing rapid transport of nutrients, contaminants or pathogens to
ground and surface water (Flury and Flühler, 1994). Preferential flow, which
includes macropore flow, fingering and funnel flow, occurs via earthworm
burrows, root channels, soil fissures and cracks, soil heterogeneity and
wetting front instabilities (Bundt et al., 2001; Darnault et al., 2003). The
occurrence of preferential flow is critically dependent on the conditions at the
soil surface such as soil water content, macropore aeration, and surface
micro-topography (all influenced by soil type and physical properties), rain
intensity (Chu et al., 2003; Mawdsley et al., 1996; Paterson et al., 1993; Van
Elsas et al., 1991), and the hydraulic properties of the pathways, as
influenced by pore size distribution and conductivity (Messing and Jarvis,
1993). An understanding of preferential flow and the implications for pathogen
movement are important for effective risk assessment (Natsch et al., 1996;
Smith, 1985).
E. coli has been widely characterized and it is often used as a model
organism for the investigation of bacterial transport in environmental systems
(Passmore et al., 2009; Powelson and Mills, 2001). The transport of
landspread E. coli in soils is of importance, both with regard to its role as an
indicator of faecal contamination in the environment, and due to the
pathogenic nature of certain strains (Committee on Indicators for Waterborne
Pathogens, 2004). While preferential flow routes have been shown to be
highly important pathways for E. coli (or faecal coliform) movement through
soils (Abu-Ashour et al., 1998; Geohring et al., 1998; Smith, 1985; Unc and
Goss, 2003), the majority of studies reported thus far have concentrated on
the quantification of the fast break through of bacteria, characteristic of
preferential flow, at scales ranging from soil core (Artz et al., 2005; Darnault et
al., 2004; Smith, 1985) to field (McCarthy and McKay, 2004; Unc and Goss,
2003). In-situ field trials where the pathways and mechanisms of bacterial
transport within soil can be related to visualised infiltration patterns are rare
(Natsch et al., 1996; Passmore et al., 2009). The use of dye for the
visualisation of infiltration patterns in soil is an established method of
indicating the presence and extent of preferential flow pathways (Kramers et
al., 2009; Weiler and Fluhler, 2004). Vitasyn blue, otherwise known as Brilliant
Blue (BB), is a dye commonly used for this purpose. Ideal dye tracers should
have good visibility in soil, should be non toxic and should have transport
properties similar to the substance of interest such as water, chemical or
biological agents (Flury and Flühler, 1994). Brilliant Blue has low toxicity, is
clearly visible in soils at concentrations of 1-4 g L-1, is soluble in water and is
neutral or anionic, and thus not subject to strong adsorption by negatively
charged soil components (Flury and Flühler, 1995).
The distribution of BB in soils has been found to be strongly correlated
with pathways of water and solute movement (Burkhardt et al., 2008).
However, several studies have found that BB is retarded relative to the
wetting front and anionic salts (e.g. Br-, I-) that move similarly to water (Flury
and Flühler, 1995; Nobles et al., 2010; Perillo et al., 1998). The capacity of
BB to predict the movement of colloidal particulates including bacteria
(Burkhardt et al., 2008; Passmore et al., 2009) has been investigated but its
predictive ability would be expected to vary with soil type and soil moisture
content as these are known to affect the transport of microbial pathogens in
soil (Chu et al., 2003; Mawdsley et al., 1996; Paterson et al., 1993; Van Elsas
et al., 1991). Additionally, adsorption and transport of BB in soils has been
reported to be effected by soil type, organic matter content and antecedent
soil moisture content (German-Heins and Flury, 2000; Ketelsen and Meyer-
Windel, 1999; Kramers et al., 2009). The objective of this study was to
investigate the predictive value of BB and Br for characterising E. coli
transport pathways in the vadose zone of different soil types, with low
intensity artificial rainfall and different antecedent moisture regimes.
Specifically, we were interested in finding out whether: (1) BB dye patterns
are predictive of E. coli distribution and transport in a soil profile; (2) the
predictive value of BB tracing for characterising E. coli transport in soil will
vary by soil type (specifically the macropore structure of the soil type) and
initial moisture content; (3) non-reactive bromide is a better predictor of E. coli
distribution in soil than BB and (4) whether either tracer provides a
conservative estimate of the risk of E. coli transport to shallow groundwater.
Materials and methods
Toxicity Testing
Toxicity effects on bacteria may be strain specific and should therefore be
tested on a case-by-case basis. A toxicity test was carried out in this
experiment to establish whether there was an adverse effect of the BB/Brmixture (applied simultaneously with the E. coli in the application matrix) on
the survival of the E. coli isolate (IND1, isolated from a human host in
University College Dublin) used in the field trials. The E. coli was grown in
Tryptone Soya broth (TSB) at 37°C for 15 h. 25 ml of the culture was added to
225 ml (1: 9 ratio) of sterile Phosphate Buffered Saline (PBS). 100 ml of this
solution was added to an equal volume of treatment solution or the control,
and E. coli numbers were enumerated at 1, 6, 24 and 48 h on MacConkey
(Oxoid) agar using the spread plate technique. Two experimental treatments
(T1: 45 g L-1 BB & 2 g L-1 KBr; T2: 60 g L-1 BB & 4 g L-1 KBr) were compared
against a control (deionised water) with three replicates for each treatment.
Preparation and enumeration of the application matrix
TSB (500 ml) was inoculated with the E. coli isolate and the culture was
incubated at 37°C for 15 h (stationary phase) in a shaking waterbath.
Thereafter, 100 ml of the broth was transferred into each of two or three
Erlenmeyer flasks containing 900 ml of sterile PBS (1: 9 ratio). For
experiments carried out near the laboratory two flasks were prepared. These
flasks, for spreading on each of two experimental plots, were enumerated
immediately before spreading. For experiments further away from the
laboratory three flasks were prepared and enumerated simultaneously before
the departure of two flasks for the trial site. The remaining flask was
enumerated again at the time of spreading, and the change in microbial
loading in flask three between the initial enumeration and spreading was used
to estimate microbial loadings for the two flasks applied at time of spreading.
Enumeration of the microbial loading in matrices to be spread was
carried out by plating 0.1 ml volumes of the matrix on MacConkey agar, after
serial dilution with PBS, using the spread plate technique, and incubating for
24 h at 44°C before counting colonies. Directly before application, the 1 L
volume of the E. coli matrix was added to an equal volume of a solution of 60
gL-1 of blue dye (BB FCF, FD& C Blue 1, C.I. Food 2, 42090) and 1.12 gL-1of
KBr, resulting in a final applied concentration of 30 gL-1 of BB and 375 mgL-1
of Br.
Field Trials
Trials were conducted at three research farms in Ireland: Johnstown Castle
(JC) (6°30’W, 52°17’N), Grange (G) (6°36’W, 53°3’N), and Moorepark (M)
(8°15’W, 52°10’N). The sites had different soil types and drainage
characteristics, which are summarised in Table 1, but trials were all flat and
under permanent grass management for either beef or dairy production.
Information on the soil physical properties at the sites such as structure,
porosity and bulk density, can be found in Kramers et al. (2009). Trials were
carried out under wet soil moisture conditions in the spring (March-April) and
dry soil moisture conditions in late summer (August-September) to ascertain
the effect of soil moisture conditions on transport prediction using BB. The
grass was cut to a height of 6 to 7 cm before the even distribution of the
application matrix over an experimental area of 1 m x 0.5 m with a watering
can and sprayer attachment. Two hours after spreading, 28 mm of rainfall
equivalent, equating to rainfall event with a 2 to 5 year return period in Ireland
(Fitzgerald, 2007), was irrigated evenly over the plot areas for 7 h (4 mm h -1)
with a with a drip irrigator rainfall simulator (Bowyer-Bower and Burt, 1989),
while ensuring no ponding occurred. This was to simulate a rainfall event
shortly after the landspreading of soiled water. The experiment was
conducted simultaneously on 2 plots for each trial using identical irrigators.
Two trials were conducted for each soil type and application season, resulting
in 4 replicate plots (with the exception of the G spring application trials where
n=2 due to flooding during the second trial). After 12 h post cessation of
irrigation (21 h after application), the plots were manually excavated and
vertical dyed profiles (average of 4.4 per plot), 10-20 cm apart, were prepared
by shaving off the soil with a knife until the profile was vertical and the
smearing was reduced to a minimum. The profiles were then photographed in
a frame measuring 35 cm x 75 cm. All profiles were photographed and one
profile for each plot was sampled. Soil samples, representative of different
degrees of dye exposure, were randomly taken from 4 cm2 sections of the
vertical soil profile within the frame and their location within the profile was
recorded. Two samples were taken from each 4 cm2 section, with a sterile
spatula. A small sub-sample for microbial and bromide analysis was taken
initially in close proximity to the surface area which was photographed.
Subsequently, a larger sample was taken within the same area for moisture
content analysis. Control samples were also taken at intervals down the other
sides of the pit to determine background levels of E. coli and Br in the soil.
Samples were stored in sterile bags and transported as rapidly as possible in
a cool box to the laboratory. All samples where were processed the day of
sampling.
Laboratory analysis
About 1.5 g of soil (fresh weight) was transferred into sterile containers for E.
coli analysis. Bacterial extraction was carried out by adding 100 ml of PBS to
each sample and subsequently shaking on an end-over-end shaker for 30
min. Dilutions of the extraction mixtures were carried out with sterile distilled
water and enumerated using Idexx Colisure™ most probable number
methodology (MPN) (McFeters et al., 1997) by incubating at 35°C for 24-48 h.
Colony forming units (CFU) per gram of soil were calculated by dividing the
MPN counts by the sample dry weight. The remainder of the soil samples
were used to determine moisture, Br and BB content. Moisture content
analysis for pre and post experimental samples was conducted by drying
samples for 48 h (or until constant weight was achieved) at 105°C, and was
calculated as (sample fresh weight - sample dry weight)/ sample dry weight.
For Br and BB analysis, 10 g soil samples were placed in 50 ml of deionised
water and shaken in an end-over-end shaker for 1 h. Thereafter, they were
allowed to settle for 2 h and filtered consecutively through a Whatman
Ashless (Grade 41, 20 µm) and a filtropur S filter (0.45 µm). Control solutions
with known concentrations of BB and Br were analysed before and after
filtering to test for any adsorption effect of the filters. Br concentrations were
measured using an ion chromatography system (Metrohm 790, Herisau,
Switzerland) with a conductivity detector and an analytical column (ICSep
AN2, transgenomic). Standard solutions were prepared from a certified Br
stock standard and the detection limit of the method was 0.025 mg L-1. BB
concentrations were determined by measuring the extracted samples at 630
nm (Flury and Flühler, 1995) with a UV-visible spectrophotometer (Varian
Cary 50, Mulgrave, Australia) and comparing with standards of known
concentration.
Statistics
Statistical analysis of toxicity data was conducted by comparing E. coli count
numbers with a repeated measures spatial-type covariance model using Proc
Mixed (SAS 9.1). We tested the hypothesis that there was no toxicity effect.
Proc Glimmix (SAS 9.1) was used to slice the interaction into simple effects.
Residual checks showed no evidence that the assumptions of the analysis
were not met. Data obtained from the field trials was analysed using Proc
mixed (SAS 9.1), with log-transformed E. coli, log BB, log Br, log (E. coli/BB)
ratio, log (E. coli/Br) ratio and maximum depth as the responses of interest.
Responses were log transformed to achieve constant variance. The structural
factors in the experiment were soil type, application season and their
interaction. The null hypothesis for each response was no effect of these
factors. It was anticipated that soil depth and moisture content of the locations
sampled could influence the responses and these measurements were
therefore tested as covariates before finalising the analysis model. In order to
avoid local, surface effects of the applications, where the entire profile was
dyed, only samples at depths greater than 10 cm with detectable E. coli and
BB were modelled. E. coli used in all models was normalised for the number
of E. coli spread on individual plots, which differed slightly between trials. As
the responses from different depths within a profile were correlated, a
covariance structure was employed to model this but convergence problems
made it difficult to fit structures other than compound symmetry. Soil type and
application season were considered as block effects and therefore inference
on these factors was of association rather than causation. Within each model
multiple comparison post-hoc analyses, using the Tukey method, were
performed on soil type and application season to indicate patterns of
association. Correlations between E. coli, BB and Br was carried out using
Proc Corr (SAS 9.1), while regression analysis using Proc mixed was used to
explore the relationship between BB and Br.
Results
Toxicity testing
There was no significant (α=0.05) treatment effect of BB /Br on E. coli
numbers and therefore no toxicity effect (Figure 1). There was a significant
time by treatment interaction (p=0.004), with a significant difference between
control and T2 at 48 hr (p=0.0001).
Field trial data
The average E. coli applied on the field plots was 1.2 x 108 CFU ml-1 (s.d.
2.04 x 108 CFU ml-1). E. coli transport by preferential flow was observed in all
three soil types in both the spring and late summer trials. To illustrate this, an
example of a dyed soil profile with superimposed E. coli sample counts is
depicted in Figure 2. While samples containing E. coli were generally
associated with dyed flow pathways, positive E. coli samples were also found
in non-dyed sections of profiles within close proximity of dyed areas,
suggesting lateral transport of E. coli into the soil matrix. A total of 266
samples were found to be positive for E. coli or BB. Of these positives 200
had both present, 23 had only BB and 43 had only E. coli (Table 2). On
average 75% of samples had co-occurrence of E. coli and BB, and this varied
between soil type and application timing (53 to 89%). The lowest cooccurrences were observed in the G soil. The presence of BB in the absence
of E. coli was observed only in summer applications. A total of 258 samples
were found to contain either E. coli or Br. Of these 145 had both present, 15
had only Br and 98 had only E. coli. Overall Br and E. coli co-occurred in 65%
of samples, ranging between 40 and 71% within soil type and season. 29 to
60% of samples were positive for E. coli in the absence of Br. 224 samples
were found to contain either BB or Br. Of these 159 were positive for both, 1
had only Br and 98 had only BB. 18 to 35% of samples within a soil type and
season were found to be positive for Br in the absence of BB. Across the 3
sites 44% and 71%, respectively, of samples indicating a maximum E. coli
depth had no co-occurrence of BB or Br.
Field trial analysis
Experiment Factors (block effects)
The F statistics of soil type and application season were used as indicators of
block effects. While p values for block factors cannot be interpreted in the
same way as randomised treatments, multiple comparison procedures were
used as a general indicator of which differences between means were of
importance. In the analysis of log transformed E. coli the F statistics of soil
and application season (3.54 and 7.59, respectively) indicate strong effects. In
this case, differences in E. coli between G and M soils (p=0.0417) and
between spring and summer applications (p=0.0416) were evident. In the
analysis of log transformed BB the F statistic of the soil by application season
interaction term shows a substantial block effect (F=4.61). Posthoc multiple
comparison analyses of the blocking factors found no large differences. In the
analysis of log transformed (E. coli/BB) ratio the F statistic of the soil by
application season interaction term (F=6.83) shows a block effect. Multiple
comparison analyses found that within the JC soil there was an association of
the ratio with application season (p=0.0003). Within the same application
season, there was an indication of differences in spring between G and JC
(p=0.0045) and between G and M (p= 0.0022) soils, while in summer
significant differences were indicated between G and M (p=0.0003) and
between JC and M (p=0.0011) soils.
In the analysis of log Br the F statistic (F=5.27) indicated a soil type
effect, while posthoc multiple comparison analyses indicated differences
between JC and M soils (p=0.189). In the analysis of log transformed (E.
coli/Br) ratio the F statistics of the soil by application season interaction term
was 4.57, indicating strong effects. Multiple comparison analyses found that
within the JC soil there was an association of the ratio with application season
(p=0.0077). Within the spring application season there was an indication of
differences between G and JC (p=0.0161) and between G and M (p= 0.0451)
soils, while in summer significant p values indicated differences between G
and M (p<0.0001), JC and G (p=0.0030), and JC and M (p=0.0269) soils. In
the analysis of maximum depth reached by E. coli within each profile
sampled, the F statistic was large (F=5.27) for the soil x application season
interaction term. Multiple comparison analyses found that within the J and M
soils there was an association with application season (p=0.0049 and
p=0.0115 respectively). Within the same application season, there was an
indication of differences in spring between G and JC (p=0.0050) and between
JC and M (p=0.0013) soils, while in summer significant p values indicated
differences between G and M (p=0.0198) and JC and M (p=0.0076) soils.
Statistical evaluation of covariate relationships
Covariates found to be important in each response analysis and their
relationship form are shown in Table 3. Due to danger of spurious covariate
relationships being detected in a small data set, all significant fits were verified
graphically insofar as possible and interpreted as a confirmation or refutation
of scientific understanding of the relationships involved. Predictions are
presented as exploratory analysis with the focus on the general form of the
relationship rather than the detail of the fitted values. Relationship forms
outlined in Table 3 have been depicted in Figure 3 to 7 following back
transformation. A statistical prediction of the relationship between E. coli
concentrations and moisture content is shown in Figure 3. In all soil types, at
both application seasons, there was a trend of increasing E. coli
concentrations to a peak moisture concentration of 0.3-0.4g g-1 dry soil and
thereafter a decrease. The extent of this trend was more pronounced in
summer than spring. This trend is greatest in the G soil and smallest in the M
soil. The statistical relationship between moisture content and the E. coli/BB
ratio is shown in Figure 4. In summer, an increase in the ratio is observable
until a peak moisture content of between 0.2-0.3 g g-1 dry soil, followed by a
decrease thereafter, in both JC and G soils. A similar pattern is observed in G
spring, albeit to a lesser degree. A very slight relationship only was
observable between the ratio and moisture content in spring in the JC soil,
and in both spring and summer in the M soil. As the moisture content
depended on application season, a comparison of the fit of the full model with
the model without application season found that application season
contributed extra information compared to what could be attributed to
moisture alone.
The statistical relationship between moisture content and the BB/Br
ratio is shown in Figure 5. In the G soil there is a decrease in the BB/Br ratio
with increasing moisture content at both application timings (to a lesser extent
in spring) to a moisture content of 0.4 g g-1. This is a linear relationship when
plotted on a semi log scale. The other soil types show a very slight similar
relationship, which is greatest in J at the spring application. A statistical
prediction of the relationship between BB and soil depth is depicted in Figure
6. There is an overall decrease in BB concentration with depth to a depth of
60-70 cm. For both M and G soils the BB concentration is lower in spring than
in summer, while the opposite is evident in the JC soil. A statistical prediction
of the relationship between depth and the BB/Br ratio is shown in Figure 7. At
both application timings there is a decrease (linear on a semi-log scale) in the
BB/Br ratio with depth in the G soil. The other soil types show a similar
relationship (that is most pronounced in J following the spring application) but
to a much lesser degree.
E. coli, Br and BB correlations
Overall correlation analysis on non-zero samples (excluding near surface
results) found significant correlation coefficients between Br and BB, E. coli
and Br, and E. coli and BB, of 0.91, 0.59 and 0.56, respectively. When broken
down by soil type and application season combinations Br and BB had
significant correlations ranging between 0.84 and 0.98, E. coli and Br had
correlations ranging between 0.63 and 0.92, and E. coli and BB had
correlations ranging from 0.65 to 0.81. This analysis provides evidence that in
a particular set of conditions there is a useful relationship between E. coli and
BB or E. coli and Br but the relationship between BB and Br is consistent
across the range of conditions examined here. A regression analysis found
that the relationship between BB and Br was not dependent on soil type or
application season. There was no variation in slope with soil or application
season, and no curvature in the relationship was detected.
Discussion
Toxicity of the BB/Br- application matrix to E. coli was found to be low and
unlikely to have had any effect on the field results. The concentrations of BB
and Br tested in the laboratory were much higher than those applied in the
field trials and thus gave a conservative estimation of any toxicity effects.
While toxicity effects may be experienced by contact for about 48 h with BB/Br
at high concentrations, the concentrations used in the field trials were lower
and the contact time of the E. coli with the concentrated matrix was limited to
the two hour period prior to rainfall simulation. Therefore any effect could be
ignored.
We evaluated the idea that BB dye patterns are predictive of E.
coli distribution and transport in the vadose zone. Preferential flow was found
to be important in the transport of E. coli through the 3 grassland soils studied,
with E. coli transport being strongly associated with patterns of active
preferential flow revealed by BB tracer. International evidence indicates that
preferential flow is the rule, rather than the exception, in most structured soils
(Flury et al., 1994; Morales et al., 2010; Smith, 1985). This is due to the fact
that only pores that are several times larger than the dimensions of a
particulate such as a micro-organism allow transport over significant distances
(Unc and Goss, 2004). E. coli concentrations were, for the most part, higher in
the dyed parts of the soil profiles than in the rest. However, similar to the
findings of Natsch et al. (1996) with Pseudomonas fluorescens, high
concentrations of E. coli were found in non-stained areas of the soil profile
close to macropores. These concentrations reduced with increasing distance
from the dyed flow channels, which is suggestive of active motility of E. coli or
the complete retardation of the BB signal. Active transport of bacteria may be
important for movement in suspension over short distances during, or soon
after, infiltration (Ritchie et al., 2003; Unc and Goss, 2004), although some
findings suggest there is no relationship between the presence of flagella and
the degree of bacterial movement (Gannon et al., 1991). Migration of E. coli
from the macropores into protected sites in the soil matrix may prevent their
further vertical transport (Natsch et al., 1996) and this could be a factor in the
prolonged survival of E. coli in soil (Brennan et al., 2010).
The co-occurrence of E. coli with BB (Table 2) in the majority of
samples suggests that BB is predictive of the distribution patterns of E. coli
within a soil profile. The (non-zero) correlation analysis also suggests that
under specific conditions BB can be a good predictor of E. coli distribution.
Passmore et al. (2009) also found that the concentration of colloids in soil was
generally proportional to the intensity of BB staining. This is in line with the
general consensus that bacterial transport in soils is mostly passive, being
determined by mass water flow (Artz et al., 2005; Ritchie et al., 2003; Unc and
Goss, 2004). However, the non co-occurrence (Table 2.) of E. coli in a
considerable portion of samples combined with low correlation coefficients
under certain conditions suggests that the transport properties of E. coli and
BB do not mirror each other all of the time and the factors influencing
retention and movement in soil may impact differently on each. These may
include differential effects of pore clogging, filtration, sedimentation,
dispersion, advection, adhesion, adsorption, hydrophobic and other
electrochemical interactions, and active transport of E. coli over short
distances by chemotaxis (Abu-Ashour et al., 1994; Unc and Goss, 2004). The
finding of non co-occurrence of E. coli and BB is in contrast to that reported by
Burkardt et al. (2008) who found that fluorescent microspheres were rarely
found in the absence of BB. However, factors affecting transport may impact
differently on microspheres than on a bacterial cell.
The exploratory statistical analysis (Table 3, Figures 3-7) reported also
suggest that BB and E. coli may behave differently, with moisture content
found to be significant in the E. coli analysis response (and those containing
E. coli; Figure 3-5) but not in that of the BB (Table 3). The reverse is
suggested with regard to the significance of the soil depth (Figure 6, Table 3)
at which the sample was taken. While the moisture content was found to be
important with regard to E. coli concentrations present in samples, the precise
mechanisms involved may be complex. An effect of survival on E. coli
numbers may be observed at lower moisture concentrations. At higher
moisture contents the continuous water films and low matric potentials,
required for bacterial movement, may be present in addition to surface and
solute chemistry effect enabling cells to overcome the constraining forces of
colloidal adsorption and facilitate active or passive transport. This may result
in lower E. coli concentrations in wetter samples. In contrast to the findings of
Natsch et al. (1996), when using a similar approach to trace Pseudomonas
fluorescens (CHA0-Rif), we found that average E. coli concentrations
decreased with increasing depth ranges, suggesting that there was some
retention or lateral redistribution of the bacteria into the soil matrix during
transport. While an association with depth was not found to be significant in
the final E. coli analysis response within this study, it should be noted that soil
depth and moisture content are strongly related in this data set. The effect of
retention would be expected to vary with soil structure and texture (including
clay content), which differs between soil types and soil horizons.
We also investigated whether the predictive value of BB tracing for
characterising E. coli transport in soil varied with soil type (specifically the
macroporocity of the soil) and initial moisture content. The data reported here
supports this assertion. Strong associations were indicated with soil type and
application timing (and their interaction), indicating that these blocking factors
were important in the distribution of E. coli, with the F statistic indicating an
association in each of the models. Application season as a factor was found
to contribute more to the model than could be accounted for by soil moisture
content alone and further work is required to explain this in terms of other
factors such as temperature, humidity, solar radiation etc., which contribute to
the application season effect. Additionally, differences were observed
between soils and application timings in co-occurrence (Table 3) and
correlation analysis. Differences were also observed between soil type and
season in the predicted relationships depicted in Figures 3 to 7. Soil type
affects the nature and extent of preferential flow (Aislabie et al., 2001; Flury et
al., 1994; Jarvis, 2007; McLeod et al., 2008; Natsch et al., 1996). Macropore
flow has been shown to be greatest in soils which are well structured (Aislabie
et al., 2011; Flury et al., 1994; Jarvis, 2007; McLeod et al., 2008; Natsch et
al., 1996) and this is the case in the lower regions of both JC and G soils.
Previous work (Kramers et al., 2009) on these soil types reported, based on
analysis of the dye coverage, finger numbers and finger types, that JC and G
soils demonstrated a stronger tendency towards preferential flow through
macropores than that of M, with a higher risk of active paths occurring at 0.7
m depth in the JC soil relative to the other soil types. Generally wetter initial
soil conditions would be expected to generate more macropore flow but the
effects of initial water content are complex, due to the confounding effects of
clay soil shrinkage and water repellency if present (Unc and Goss, 2003).
Previous work on these soil types reported that in summer, under the drier
initial soil conditions, there was an increased interaction between macropores
and the matrix in G and M soils than was observed under wetter initial
conditions (Kramers et al., 2009). This increase in lateral redistribution may
decrease the relative proportion of water flowing to greater depths, resulting in
higher numbers of bacterial cells being translocated into the smaller pores in
the soil matrix, where their vertical transport becomes limited.
The idea that non-reactive tracer bromide, which is believed to move
similarly to water (Natsch et al., 1996), is a better predictor of E. coli
distribution in soil than BB was not supported by the data. BB has been found
to be retarded relative to both E. coli and Br (Passmore et al., 2009; Perillo et
al., 1998) and Br has been reported to move similarly to Pseudomonas
fluorescens (Natsch et al., 1996). Br is considered to be a conservative tracer
which does not interact with the soil matrix, while BB is a reactive tracer with a
non linear sorption isotherm (Nobles et al., 2010). Over all samples, non zero
correlation coefficients with E. coli were found to be similar for Br and BB,
although the highest achieved coefficients when broken down by soil type and
season were greater for Br than BB. The best correlation was observed to be
between Br and BB. This may partially be due to the fact that neither tracer is
affected by moisture content to the same degree as E. coli (Table , figures 35). The co-occurrence analysis, which includes zero values, found that cooccurrence with E. coli was greater with BB than with Br. E. coli was also
found twice as often without Br than BB, and the percentage of samples at the
maximum transported depth of E. coli without Br was found to be greater that
for BB. BB was rarely observed in the absence of Br but Br was found without
BB in 25-36% of samples. This may indicate retardation of BB relative to the
wetting front and advection and dispersion of Br. Based on the data from the
field trials reported here we rejected the prediction that Br was a better tracer
of E. coli distribution in soil than BB. The predictive value of these
relationships however is likely to be organism specific and thus may not
pertain to all landspread bacteria, or even all E. coli strains.
In addition to investigating the overall predictive value of BB and Br in
tracing E. coli distribution in soil, we were specifically interested in whether the
tracers were good predictors of the maximum depths to which E. coli could
travel. This is a critical factor in the assessment of whether the tracers provide
a conservative estimate of the risk of E. coli transport, from activities such as
landspreading, to underlying shallow groundwater or field drains that impact
on surface water. The absence of BB in 44% of samples at the maximum
transported depth of E. coli suggests that BB may not always indicate the
extent of the risk of E. coli movement to greater depths. Others have also
reported that bacteria were transported deeper than visible dye (Natsch et al.,
1996; Passmore et al., 2009). This may be due to BB retention combined with
anion exclusion, which may enhance transport of microbes relative to water
(Flury et al., 1994; Powelson and Gerba, 1995). This is contrary to the
findings of (Nielsen et al., 2011), however, who observed undiluted
concentrations of BB and Br tracers in drainage water and a reduction in
microspheres by a factor of 150. Br was found to be a poorer indicator of the
presence of E. coli than BB at lower depths, with an absence of Br in 71% of
samples at the maximum transported depth of E. coli. Studies using dye
staining or bromide tracing in soils to identify the depths of infiltration by
preferential flow should therefore use caution when extrapolating the
distribution results to indicate risks of pathogen movement in soil as the both
tracers may represent an underestimate of E. coli transport in subsoil.
In conclusion, in this study we observed that passive transport through
preferential flow routes was the dominant mechanism of vertical E. coli
transport in the grassland soils studied. E. coli transport was mainly
associated with visualised infiltration patterns, although there was some
evidence of differential transport of BB and E. coli. Correlation and cooccurrence analysis found that both BB and Br could be good predictors of E.
coli distribution under certain conditions, but the usefulness of the relationship
was affected by a number of factors. Soil type and application season were
important in the distribution of E. coli (including its maximum depth) and the
predictive relationship between the bacterium and its tracers. Moisture content
was found to be an important covariate for interpretation of the E. coli results
and therefore the relationship between E. coli and BB, and the extent of effect
varied with soil type. Br was not found to be a better predictor of E. coli
distribution than BB. The deeper migration of E. coli than both tracers in
numerous profiles indicates that caution should be used in extrapolating BB
and Br test results to pathogen movement in soil, as this could result in an
underestimation of the depth of transport and risk to underlying water bodies.
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Figure/Table Legends
Table 1. Major soil properties of the sites and soil moisture contents at the two
application seasons: summer (Sum) and spring (Spr).
Table 2. Percentage co-occurrence and non co-occurrence of E. coli, BB and
Br in positive samples for soil type and application season
Table 3. Covariates found to be important in each response analysis and their
relationship form (E: E. coli count, C: constant that is the general additive
effect of soil and application season, MC: moisture content, β1 and β2:
coefficients of linear and quadratic terms respectively, D: BB concentration, d:
depth, and RED: E. coli/BB ratio, REB: E. coli/Br ratio).
Figure 1. Average E. coli survival in toxicity test with BB and bromide (T1: 45
gL-1 BB & 2 gL-1 KBr; T2: 60 gL-1 BB & 4 gL-1 KBr). Bars represent standard
error.
Figure 2. An example of a dyed soil profile with superimposed
E. coli sample counts (CFU’s/g dry soil).
Figure 3. Predicted E. coli concentrations per gram of soil (dry weight) with
varying moisture content at the two application seasons: summer (Sum) and
spring (Spr).
Figure 4. Predicted E. coli/BB ratio with varying moisture content the 3 studied
soils at the two application seasons: summer (Sum) and spring (Spr). The
predicted curve of JC spring mirrors exactly that of M spring and so is not
observable.
Figure 5. Predicted BB/Br ratio with varying moisture content the 3 studied
soils at the two application seasons: summer (Sum) and spring (Spr).
Figure 6. Predicted BB concentrations per gram of soil (dry weight) with
varying soil depth the 3 studied soils at the two application seasons: summer
(Sum) and spring (Spr).
Figure 7. Predicted BB/Br ratios with varying soil depth the 3 studied soils at
the two application seasons: summer (Sum) and spring (Spr).
Table 1.
Site
Soil
Type
(WRB)
Parent
Material
Johnstown
(JC)
Luvic
Gleysol
Glacial
deposits
Grange
(G)
Haplic
Luvisol
Glacial
deposits
Moorepark
(M)
Haplic
Cambisol
Sandstone
Depth
(cm)
0-10
10-35
35-75
75100
0-15
15-45
45100
0-15
15-55
55100
Sand
%
67
55
42
42
Silt
%
21
27
31
31
Clay
%
13
18
27
27
OM
%
6.3
2.7
2.2
2.2
Depth
(cm)
0-35
35-75
Initial
Soil
Moisture
%
Spr Sum
29 19
34 31
37
32
28
39
42
40
24
26
32
7.4
3.5
2.1
0-15
15-45
32
35
22
26
53
55
56
31
37
37
16
8
7
8.5
3.7
2
0-15
15-55
33
26
15
14
Table 2.
% occurrence
G
Spr
G
Sum
G
Total
JC
Spr
JC
Sum
JC
Total
M
Spr
M
Sum
M
Total
Overall
Total
BB and E. coli present
60.0
53.5
56.2
84.6
79.5
82.3
88.9
74.4
82.5
75.2
BB present, no E. coli
0.0
39.5
23.3
0.0
2.3
1.0
0.0
11.6
5.2
8.6
E. coli present, no BB
40.0
7.0
20.5
15.4
18.2
16.7
11.1
14.0
12.4
16.2
Br and E. coli present
40.0
40.0
40.0
71.2
53.5
63.2
57.4
66.7
61.3
56.2
Br present, no E. coli
0.0
35.0
20.0
0.0
0.0
0.0
0.0
2.6
1.1
5.8
E. coli present, no Br
60.0
25.0
40.0
28.8
46.5
36.8
42.6
30.8
37.6
38.0
BB and Br present
66.7
75.0
72.4
80.0
63.9
72.8
64.6
73.0
68.2
71.0
BB present, no Br
0.0
0.0
0.0
2.2
0.0
1.2
0.0
0.0
0.0
0.4
Br present, no BB
33.3
25.0
27.6
17.8
36.1
25.9
35.4
27.0
31.8
28.6
Table 3.
Analysis variable
log E. coli
log BB
log (E. coli/ BB) ratio
log Br
log (E. coli/ Br) ratio
Maximum depth
Significant Covariates tested
moisture content
moisture content squared
depth below soil surface
depth squared
moisture content
moisture content squared
None
moisture content
P values
0.0005
0.0001
0.0041
0.0315
0.0315
0.0015
n/a
0.0003
Depth below soil surface
None
0.0216
n/a
Relationship form
E  e C  1MC   2 MC
2
D  e C  1d   2d
2
RED  eC 1MC2MC
REB  e C  1MC 2d
2
Figure 1
E. coli CFU's/ml
1e+9
1e+8
1e+7
0
10
T1
T2
Control
20
30
Time (hrs)
40
50
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7