Large tank experiment on nitrate fate and transport:

the role of permeability distribution

M. Mastrociccoa, N. Colombani

#a,b, S. Palpacelli

c, G. Castaldelli

d

a Dipartimento di Scienze della Terra, Università di Ferrara, Ferrara, Italy

b Dipartimento di Scienze della Terra, Università ”Sapienza” di Roma, Roma, Italy

c Dipartimento di Scienze dei Materiali Università Politecnica delle Marche, Ancona, Italy

d Dipartmento di Biologia, Università di Ferrara, Ferrara, Italy

________________________________________________________________________________

# corresponding author

Abstract

A long-term elution experiment to study the saturated transport of pre-accumulated fertilizers by-

products, was conducted within a large tank (4x8x1.4 m) equipped with 26 standard piezometers.

Sandy sediments (35 m3), used to fill the tank, were excavated from an unconfined alluvial aquifer

near Ferrara (Northern Italy); the field site was connected to a pit lake located in a former

agricultural field. To evaluate spatial heterogeneity, the tank’s filling material was characterized via

slug tests and grain size distribution analysis. Initial tank pore water composition exhibited high

concentration of nitrate (NO3-) sulphate (SO4

2-) calcium (Ca

2+) and magnesium (Mg

2+), due to

fertilizer leaching from the top soil in the field site. The initial spatial distribution of NO3- and SO4

2-

was heterogeneous and not related to the finer grain size content (<63μm). The tank’s material was

flushed with purified tap water for 800 days in steady state conditions; out flowing water was

regularly sampled to monitor the migration rate of fertilizer by-products. Complete removal of NO3-

and SO42-

took 500 and 600 days, respectively.

Clean-up times were compared with those of a previous laboratory experiment developed on

columns (1x0.1 m) filled with the same sediments. Results emphasized organic substrates

availability and spatial heterogeneities as the most important constraints to denitrification and

nitrogen removal, which increase the time required to achieve remediation targets.

Keywords: fertilizers; aquifer; pollution; transport; heterogeneities; modeling.

1. Introduction

Nitrate contamination of groundwater has become one of the most serious environmental concerns

in industrialized countries. The negative effect of fertilizers and pesticides leaching into aquifers

from agricultural activities has been intensively investigated (Chen et al. 2007, Böhlke et al. 2007,

Postle et al. 2004, Nolan et al. 2002, Puckett et al. 2002). While the export of contaminants into

surface waters (McMahon and Böhlke 1996) have immediate effects on aquatic ecosystems such as

eutrophication (Lucassen et al. 2004, Iversen et al. 1998, Lamers et al. 1998), effects on

groundwater quality are not as apparent and may be underestimated, with the risk of long term

contamination and health consequences for future generations (Kraft et al. 2008). For instance, NO3-

contamination of drinking water is known to cause methemoglobinemia in infants (Cynthia et al.

2002), while elevated concentrations of SO42-

may cause diarrhea (WHO 1996). In many countries a

significant portion of drinking water is exposed to these threats as evidenced by recent European

law adjustments for water protection, from the Nitrate Directive (91/676EEC, OJEC 1991) to the

Water Framework Directive (2000/60 EC) which specifically address this issue.

Nitrogen is the most widely used fertilizer and is added to soil in different forms and oxidation

states, depending on soil features, crop needs, and local convenience to use manure or synthetic

compounds. In the top soil microbial activities rapidly transform nitrogen to nitrate (Fenchel et al.

1998). Once infiltrating waters enter the subsurface, oxygen (O2) may rapidly decrease and NO3-

can be used by denitrifying bacteria as electron acceptor (Appelo and Postma 2005) in organic

matter and sulphides oxidation processes (Aravena and Robertson 1998). If these substrates are not

abundant, denitrification occurs at low rate and, therefore, NO3- can travel long distances within

aquifers (Shomar et al. 2008, Taylor et al. 2006, Andersen and Kristiansen 1984), depending on

aquifer materials and structure heterogeneities. If this happens, then to resolve the direction and rate

of groundwater flow and to predict solute transport at various spatial and time scales, hydraulic

conductivity (k) distribution has to be ascertained (Gelhar 1993, Webb and Davis 1998).

For this purpose, a large tank was filled with fertilizer contaminated aquifer materials, whose grain

size distribution and permeability was intensively characterized to obtain an accurate estimate of

sediment heterogeneities. Multi level slug tests were used to estimate k values along piezometers

depth (Rus et al. 2001). To asses the role of heterogeneities on remediation efficiency, the sediment

clean-up time was estimated by flushing the tank with purified tap water and the results were

modelled and discussed in comparison with those from previous column experiments performed on

the same aquifer material but flushed at higher flow rate (Mastrocicco et al. 2008).

2. Materials and methods

2.1. Field site

The sediments were excavated from a sand pit (in the Po Plain, Italy) located along a palaeo-

meander bend of the Po River (Mastrocicco et al. 2008). The principal Quaternary lithofacies of the

area include coarse-grained facies associations (fluvial-channel and crevasse sands) and fine

grained ones (floodplain, prodelta and marsh deposits) (Amorosi et al. 2003). Associated with the

coarse-grained facies, is the most productive aquifer, consisting of a 20-25 m thick Holocene sandy

succession extending all over the area (Stefani and Vincenzi 2005). This aquifer is usually confined

by a thick clay layer, which is often absent near the major paleo-channel bodies hence pollutants

may penetrate downwards into the unconfined aquifer.

The excavation was completed in 2 hours on March 8th

2006, using a crane excavator; initially the

top 0.5-0.6 m of weathered soil was dug, then saturated sediments from 0.5-0.6 m below ground

level down to 1.9-2.0 m were collected and immediately displaced into the tank located at the

Hydrogeology Laboratory of the University of Ferrara. During the excavation, a geological survey

was carried out in order to recognize sedimentary structures. The heterogeneous nature of the

sedimentary succession, with upward-coarsening silt and sand sediments, gradational lower

boundaries and sharp tops, indicates a crevasse splay facies (Amorosi and Marchi 1999).

2.2. Tank set up

The experiment was performed in a large tank (4 m wide by 8 m long and 1.4 m deep) located in the

Hydrogeology Laboratory at the Scientific and Technological Pole of the University of Ferrara (Fig.

1). The tank was assembled with an internal structure of armed PVC fastened on an external

structure of natural wood; the non metallic materials were selected for future electrical resistivity

tomography applications. The tank was filled with 42 m3 of unconsolidated material (35 m

3 of

natural sediments and 7 m3 of gravel), by means of a bulldozer equipped with a 2-tons tilting shovel

mounted on a 8 m long telescopic crane.

Saturated natural sediments were poured by the tilting shovel into the tank starting from the inflow

gravel wall towards the outflow wall. Once a layer of 0.2±0.05 m was created, it was compacted,

using the bottom of the shovel, and new layers were added. The filling procedure took 10 hours.

Following this, the natural compaction of sediments was monitored for four months; the average

bulk compaction was found to be approximately 0.02 m.

An external reservoir (constant head) was connected to the tank via three inflow pipes (Fig. 1); the

reservoir had a large surface area to minimize the introduction of trapped air bubbles. The constant

head was introduced to create a steady state flux with a mean head gradient of 7% in the sediments;

in order to maintain a certain uniformity of the potentiometric surface, two gravel walls were built

one at the inflow and the other at the outflow of the tank; the mean head gradient in the gravel walls

was calculated by the Darcy law to be 0.1‰.

Figure 1: plan view of the tank with the constant head reservoir on the left hand side, creating a flux

towards the outflow pipes on the right; gravel walls are represented near the inflow and the outflow

walls; 1 inch and 2 inches piezometers are plotted with circles and triangles, respectively; the origin

of the x, y, z axis was located to the left bottom corner of the inflow wall.

Twenty-six piezometers were installed using a hand driven auger, on the base of a semi-regular

monitoring grid (Fig. 1). A detailed topographic survey was carried out using a Nikon DTM-450

total station, to accurately determine the well case position in x, y, z axis (Fig. 1); piezometric heads

were monitored every 2 months.

Seventy-eight undisturbed 1 inch cores were collected every 0.3 m by a Shelby sampler for grain

size analyses. Particle size curves were obtained using a sedimentation balance for the coarse

fraction and an X-ray diffraction sedigraph 5100 Micromeritics for the finer fraction; the two

regions of the particle size curve were connected using the computer code SEDIMCOL. Bulk

density and total porosity were determined gravimetrically; the organic carbon content (foc) of the

sediments was measured by dry combustion.

To estimate the k variability 130 multi level slug tests were performed within the saturated zone

(every 0.15 m) using small inflatable straddle packers. The tests were run after the complete elution

of resident water in order to avoid interference with the monitoring program, due to the possibility

that slug tests could modify the flow field, although transiently. The estimated k values are valid

only in the vicinity of the borehole, because k values may be biased by multiple factors; usually any

bias is presumably towards lower hydraulic conductivity (Zlotnik 1994, Hyder and Butler 1995,

Butler et al. 1996). The greatest potential bias is a true well-skin, a zone of altered hydraulic

conductivity immediately surrounding the well bore caused by drilling disturbance. To minimize

this problem, piezometers were modestly developed to virtually eliminate all skin effects (Rovey

and Niemann 2001) during slug testing. Water level in every piezometer was instantaneously

lowered with a pneumatic syringe. All the acquired slug test responses were analyzed using the

Bouwer and Rice method (Bouwer and Rice 1976).

Parameter Natural sediments Gravel walls

Grain size (%)

Gravel (2000-20000 μm)

Coarse sand (630-2000 μm)

Medium sand (200-630 μm)

Fine sand (63-200 μm)

Silt (2-63 μm)

Clay (< 2 μm)

Hydraulic conductivity (m/s)

Bulk density (Kg/m3)

Total porosity (-)

Organic carbon (-)

0.0

0.30

5.4

50.8

32.1

11.4

1.8e-6

1.68

0.42

0.027

35.0

3.3

22.3

22.8

13.4

3.2

1.2e-3

1.61

0.27

0

Table 1: average grain size distribution, hydraulic conductivity, bulk density, total porosity and foc

measurements for natural sediments and gravel walls.

Once the tank elution started, groundwater sampling took place every 2 months in order to get a

snap shot of the distribution of solute concentrations; samples were taken from every piezometer

via low flow sampling technique using Waterra inertial pumps, from the external reservoir, from the

inflow gravel wall and from the outflow pipes .

2.3. Analytical methods

In-well parameters were determined by the HANNA Multi 340i instrument which includes a HIcell-

31 pH combined electrode with a built-in temperature sensor for pH measurements, a CellOx 325

galvanic oxygen sensor for DO measurements, a combined AgCl-Pt electrode for Eh measurement

and a HIcell-21 electrode conductivity cell for EC measurements. Samples were filtered through

0.22 μm Dionex vial caps. The major cations anions and oxianions (acetate and formate) were

determined by an isocratic dual pump ion chromatography ICS-1000 Dionex, equipped with an

AS9-HC 4 x 250 mm high capacity column and an ASRS-ULTRA 4mm self-suppressor for anions

and a CS12A 4 x 250 mm high capacity column and a CSRS-ULTRA 4mm self-suppressor for

cations. An AS-40 Dionex auto-sampler was employed to run the analyses, Quality Control (QC)

samples were run every 10 samples. The standard deviation for all QC samples run was better than

4% relative. Charge balance errors in all analyses were less than 5% and predominantly less than

3%. Alkalinity content was determined using a Merk Aquaquant titration package.

2.4. Geostatistical Analyses and Three-Dimensional Visualization

To describe grain size distribution analysis, the median of the average grain radius expressed in mm

(M), the uniformity coefficient (U) which is the ratio between d10 and d90 (d10 and d90 being the

mean particle diameter in mm expressing the 10th

and 90th percentile of the cumulative curve) and

the coefficient of skewness (S) were calculated. The coefficient of skewness is a measure of

asymmetry in the distribution; a positive skew indicates a longer tail to the right, while a negative

skew indicates a longer tail to the left, while a perfectly symmetric distribution, like the normal

distribution, has a skew equal to 0. The sample skewness S is calculated as follows (King and

Julstrom 1982):

3

13

1

n

i

i xxns

S

where n is the number of data values for a sample xi, x is the sample mean and s is the sample

standard deviation. A classical statistical approach was used to infer k distribution within the tank,

using the Kolmogorov-Smirnov (K-S) statistic (Sprent 1993). K-S statistic is the largest difference

between an expected cumulative probability distribution and an observed frequency distribution.

The expected distribution is the normal probability distribution with mean and variance equal to the

mean and variance of the sample data.

The three-dimensional representation of the saturated hydraulic conductivity and the distribution of

solute concentrations was generated through the application of a quadratic inverse distance

algorithm without smoothing and with an anisotropy ratio between the y and x axis equal to 2,

accounting for tank dimensions (Deutsch and Journel 1998). The ordinary Kriging interpolation

method (Swan and Sandilands 1995) was used to represent piezometric contours, the experimental

variogram was fitted by means of a nonlinear Least Square regression method to a linear function

with slope of 1.5e-3

and anisotropy ratio equal to 2, accounting for tank dimensions.

2.5. Modeling

Assuming a uniform water content and steady-state flow conditions, the one-dimensional transport

non equilibrium convection–dispersion equation (CDE), including first-order degradation reaction,

can be written as (van Genuchten and Wierenga 1976):

mmmimmm

wm

mmm

m CCCx

CJ

x

CD

t

C

2

2

(1)

imimimimmim

im CCCt

C

(2)

where subscripts m and im pertain to the mobile and immobile region, respectively. C (ML−3

)

denotes solute concentrations as a function of distance x (L) and time t (T). Dm (L2T

-1) is the

hydrodynamic dispersion coefficient for the mobile region, Jw (LT-1

) the volumetric water flux

density and the volumes θ (L3L

−3), θm, (L

3L

−3), and θim (L

3L

−3) are the total, mobile and immobile

water content. For θm = θ, Eq. 1 reduces to the single-domain CDE. The solute-mass transfer

between mobile and immobile regions is limited by the first-order rate coefficient α (T−1

). The first-

order degradation rate is μ (T−1

). The elution curve of Br- from the

tank outflow was analyzed to

determine the hydrodynamic dispersion coefficient D. The CDE was solved by the code CXTFIT

2.1 (Toride et al. 1999) in estimation mode to fit observed concentrations. Subsequently, the

degradation rate μ was estimated from the NO3- elution curve. The column elution was simulated

following the non equilibrium CDE, in estimation mode to fit observed Br- concentrations to the

three unknown parameters D, θm and α. Subsequently, degradation rates μm and μim were estimated

from the NO3- elution curve.

3. Results and discussion

3.1. Tank characterization

The grain size analysis of the 78 samples (Fig. 2) showed that sediments came from the same

depositional environment, identified as a crevasse splay. Because this depositional environment is

characterized by a broad range of textures, due to traction and traction plus fall-out processes, a

considerable variability in the grain size distribution was registered (Amorosi and Marchi 1999).

The median of the average grain radius varied from 0.043 to 0.107 mm, typical of very fine sands;

U varied from 1.65 to 3.12 depicting different sorting of the samples, S remained within 0.40 to

0.84, indicating a quasi-normal distribution with small tailing towards the large grain size.

Following the Wentworth classification these sediments can be defined as silty-sands.

Figure 2: cumulative grain size distribution curves of all the 78 samples analyzed, on the left;

Wentworth classification triangle, on the right.

The k values measured in the tank range from 3.2e-7

to 1.7e-5

, spreading over two orders of

magnitude, with an average k of 3.1e-6

m/s, which is in close agreement with the k value of 1.8e-6

m/s, determined by applying the Darcy law to the tank’s outflow rate. The Kolmogorov-Smirnov

test revealed that the K-S statistic is larger than the critical value of the K-S statistic at 90, 95, or 99

percent significance level for the lognormal distribution (Table 2); thus, the hypothesis that the

underlying population is normally distributed is acceptable (Sprent, 1993). This is represented in the

histogram plots of Figure 3. The normal distribution exhibits a K-S statistic larger than its K-S

critical values and an elevated S value that implies a tailing towards higher k values.

Statistics Ln k k (m/s)

Minimum

Maximum

Mean

Variance

Standard deviation

Skewness

Kolmogorov-Smirnov

Critical K-S at 90%

Critical K-S at 95%

Critical K-S at 99%

-14.94

-10.98

-13.00

0.68

0.83

-0.08

0.09

0.11

0.12

0.14

3.2e-7

1.7e-5

3.1e-6

7.6e-12

2.7e-6

2.48

0.16

0.11

0.12

0.14

Table 2: summary of statistics for slug test results.

Figure 3: frequency k distribution plots of all the 130 slug tests with logarithmic and linear scale for

the x axis (left and right respectively); the lines represent normal distribution curves fitted to the

data.

Despite the considerable number of k measurements within such a small domain, it is difficult to

infer trends or deviations from the normal distribution. Although a lognormal distribution should be

assumed and the calculated sample mean and variance are representative of the population

distribution, in order to understand if there was a preferential distribution in hydraulic conductivity,

a three dimensional plot depicting isosurfaces of k equal to 5e-6

m/s was created with the inverse

distance interpolator. Isosurfaces (Fig. 4) demarcate a large zone extending from the centre to the

right side of the outflow wall where groundwater should migrate faster than in other zones: three

more zones of high permeability are also visible in figure 4, but they are smaller and probably not

connected with the main one.

Figure 4: three dimensional representation of isosurfaces of k equal to 5e

-6 m/s; the shaded volumes

indicate the gravel walls.

Tank characterization was completed by monitoring piezometric heads every 2 months. Steady state

flow was inferred as heads variation was null or within the measurement error (±15 mm). As a

standard, the heads contour recorded in April 2007 is shown in figure 5. It is clear that, although

piezometric contour lines lie roughly perpendicularly to the no flow boundaries of the tank, the

head gradient is not constant as a result of local k heterogeneities.

Figure 5: contour plot of piezometric heads in m; the shaded areas indicate the gravel walls.

In fact, the head gradient flattens in correspondence to high k zones (Fig. 4): this is consistent with

the Darcy law which states that for a given flux per unit area the head gradient is steeper for lower k

values whereas it is flatter for higher k values (Fetter 1999).

3.2. Evolution of solute concentrations distribution

The tank monitoring revealed that chloride could not be used as a tracer for the experiment, because

it was present at the same concentration in the inflow purified tap water (28-32 mg/l) and in the

resident pore water (30-33 mg/l). Instead, bromide was present at trace concentrations in the inflow

water (0.03-0.06 mg/l); while in the resident pore water (0.4-0.26 mg/l) it was nearly 1 order of

magnitude higher. Figure 6 shows a continuous decrease of Br- concentration over 600 days, when

it balanced the concentration of inflow water. This was considered to be the time required to flush

resident pore water from the tank. Excluding physical non-equilibrium or sorption processes, at the

applied flow rate of 26 l/d, this time corresponds to approximately 2 tank’s pore volumes.

Considering the Darcy law in the form of:

en

ikv

(3)

v is the average pore water velocity, i is the head gradient and ne is the effective porosity.

Referring to the average parameters listed in Table 1 and to an average head gradient of 7% in the

sediments and 0.1‰ in the gravel walls, the mean pore water velocity is approximately 2.6±0.1

cm/day in the sediments and 3.8±0.1 cm/day in the gravel walls which, when multiplied by the

length of 7 m and 1 m respectively, it gives a total travel time of 590±20 days, confirming that Br-

can be used as a conservative tracer.

The behavior of NO3- was different as a decrease in concentration was recorded from the beginning

of the experiment and NO3- reached the concentration below detection limit in approximately 500

days (Figure 6). Additionally, NO3- concentration in the inflow water fluctuated from 6 to 10 mg/l,

but after 500 days NO3- did not breakthrough again, confirming the capacity of sediments to

naturally attenuate this inorganic contaminant. The decrease of NO3- concentration, compared with

the conservative behavior of Br-, indicates a partial consumption by denitrifying bacteria, since

other inorganic processes (like pyrite oxidation) were unlikely to happen, as an increase of SO42-

concentration was not recorded. The weak NO3- removal may be attributed to a low denitrification

activity which started about 150 days after the beginning of the experiment, when oxygen became

limited, as evidenced both in the outflowing water and in the piezometers (data not shown). The

organic matter content of these natural sediments is generally low because of their high depositional

energy (Table 1) and is also explicable by the local agricultural practices, based on synthetic

inorganic fertilizers during the last 40 years. Labile organic matter and its mineralization by-

products acetate and formate remained low throughout the entire experiment (Figure 6), about two

orders of magnitude lower than the available electron acceptors, as nitrate for denitrification and

sulphate for sulphate reduction (Christensen et al. 2000).

The initial increase of acetate and formate concentration (Figure 6) is imputable to an increase of

hydrolyzation processes, probably due to sediment mixing and oxygenation during excavation,

transport and filling of the tank. Acetate and formate rapidly declined below detection limit, both

for dilution with the inflowing purified tap water and for complete mineralization, with nitrate as

the main electron acceptor. However, the limited availability of acetate, formate and organic matter

in general, prevented a shift towards more reducing conditions within the tank; this is confirmed by

slightly negative Redox potentials, measured at the outflow and in the piezometers and by the

conservative behaviour of sulphate, proving the absence of sulphate reduction. Like acetate and

formate, ammonium increased during the first 80 days, reaching a peak of 0.4 mg/l at the second

sampling. Nitrite trend was very similar in amplitude and shape but starting with null concentration

at the beginning of the experiment, when sediment mixing and oxygenation could have enhanced

denitrification.

NO2- showed a peak 100 days later than ammonium (Figure 6). In soils, nitrite may accumulate

during nitrification and denitrification (Burns et al., 1996). Since oxygen was somewhat limiting in

the tank, being available in the inflowing tap water (5±0.5 mg/l) but never present at the outflow

during the whole experiment, some nitrite accumulation may have taken course during nitrification.

However, in particular conditions of low acetate availability and very high nitrate concentrations, as

those measured in this sediment, it is possible that denitrification also could have contributed to

nitrite accumulation (Oh and Silverstein, 1999; Kelso et al., 1999).

The slow decrease of Ca2+

compared to the one of Br- could be related to dissolution of calcite, often

involved as secondary reaction during organic matter degradation (Prommer et al. 2007) which was

likely to occur during the whole experiment and would explain the maintenance of a constant

concentration notwithstanding elution.

Figure 6: time series plots of selected inorganic and organic species at the tank’s outflow, error bars

not shown for clarity.

While the elution of dissolved species was relatively homogeneous at the tank’s outflow, within the

tank evidence of the existence of heterogeneities were clear from the first sampling round. Indeed,

the initial concentration of all major cations and anions was variable within the piezometers grid

(Fig. 7), with NO3- being the most variable (135 to 380 mg/l) followed by SO4

2- (310 to 460 mg/l).

Figure 7: surface maps of selected inorganic species within the tank domain; in every graph, the

initial concentration map is transparent and the concentration map after 500 days is opaque.

No correlation was found between silt and clay contents and the initial dissolved concentrations in

each piezometer. Results in Figure 7 show that Br-

was not flushed homogeneously, since the

central portion of the tank had the lowest concentrations in accordance with the k field

representation (Fig. 4), while the left side maintained high concentrations due to the low

permeability of sediments. NO3- concentration recorded in piezometers after 500 days was also very

low or below detection limits except for the left portion of the tank near the outflow, a low k zone

where groundwater flow was nearly inhibited, as confirmed by the Br- distribution after 500 days. It

is also noticeable that although the NO3- concentration at the tank outflow extinguished after 500

days, within the tank NO3- was still present and it took more than 750 days to disappear.

SO42-

and Ca2+

showed a similar spatial pattern (Figure 7), both exhibiting the lowest concentrations

in the central portion of the tank. However, they did not approach the inflowing water

concentration, except for a narrow zone along the tank, located in the centre of the Y axis (Fig. 7),

confirming a slow release of these ions into the groundwater.

3.3. Comparison with column results

In the column, initial NO3- concentration was 199 mg/l, permeability was 4.8e

-6 m/s, porosity 0.42

and the calculated clean-up time at field conditions was 850 days (Mastrocicco et al. 2008). To

compare clean-up time at field conditions estimated via column results with the ones gained on the

basis of the tank experiment results, the CXTFIT model was applied. Model results (Table 3)

indicate that the elution of Br- from the

tank outflow can be approximated by the equilibrium CDE,

while in the column it can only be approximated by non equilibrium CDE, to avoid an unrealistic D

value (not shown). Physical non equilibrium was instilled by the elevated pore water velocity

employed, which enhanced the preferential flow through macropores. The mobile water content

within the column is considerably lower than the total water content and the low value of α limited

the exchange between mobile and immobile regions, producing a Br- decline from the beginning of

the elution (Fig. 8).

*Fitted parameter

Table 3. Summary of experimental and modeled elution curves.

Figure 8: Simulated NO3

- (solid line) and Br

- (dashed line) concentrations and observed NO3

- (filled

circles) and Br- (open circles) concentrations for the tank and column outflow.

Calculated degradation rates for NO3- in the tank and column were of the same order of magnitude,

although within the column μm was more than three times higher than in the tank; the very low μim

denotes the limited role of immobile water in the total degradation rate and the limited availability

of biodegradable organic matter within the tank, that inhibited further denitrification. In all cases, a

good agreement between calculated and observed concentrations was obtained, with R2 ranging

from 0.969 to 0.996 (Tab. 3).

Tank Column

Parameter Br- NO3

-

Br- NO3

-

V (m/d)

D(m2/d)

θm (-)

α (1/d)

μm (1/d)

μim (1/d)

2.60e-2

2.69e-2*

0.38

-

-

-

2.60e-2

2.69e-2*

0.38

-

1.93e-3*

-

1.66

0.238*

0.19

4.11e-3*

-

-

1.66

0.238*

0.19

4.11e-3*

7.08e-3*

1.04e-4*

R2 (-) 0.988 0.996 0.986 0.969

Holding the parameters of the calibrated model, an elution scenario within the tank was

implemented using the same initial NO3- concentration of the column experiment (199 mg/l) and

the regional head gradient of the local aquifer (1‰). The average time required to clean-up

groundwater from fertilizers by-products was calculated in approximately 2510 days for NO3-.

The comparison of the two different experiments to estimate the time required to flush away NO3-

from the same saturated aquifer material leads to quite dissimilar results. Namely, the estimated

clean-up time for NO3- in the tank experiment is approximately 2.9 times greater than for the

column.

4. Conclusions

A large tank experiment was implemented to evaluate the clean-up time of a shallow unconfined

aquifer contaminated by fertilizer by-products. Via grain size distribution analysis and geological

survey, the depositional environment was identified as a crevasse splay, characterized by a large

spectrum of textures and a heterogeneous k field. To overcome the common problems associated

with permeability testing on recovered samples, multi level slug tests were performed. This

technique allowed a precise three-dimensional reconstruction of the k field and its local

heterogeneities within the tank.

NO3- degradation within the tank was limited by scarce organic substrate availability, since about

40 years ago organic fertilization ceased and labile organic load underwent to a substantial

reduction and disproportion with respect to the total oxidative capacity. In agreement with results of

a column experiment, using the same sediments, nitrate fate within the tank confirmed the high

vulnerability of unconfined aquifers to this common pollutant and the long bioremediation time. A

methodological implication of this study is that laboratory experiments can provide robust results

only when the flow velocity approximates the field conditions and relatively large samples are used,

because considerable heterogeneities present in sediments need to be taken into account when

testing fertilizer fate and transport within the saturated zone in complex alluvial depositional

environments.

Acknowledgements

The work presented in this paper was made possible and financially supported by the L.A.R.A.

project, under OBIETTIVO 2 European fund (project FE-120). Prof. Torquato Nanni and Prof.

Carlo Bisci are gratefully acknowledged for their countless suggestions to improve the manuscript.

Dr. Enzo Salemi and Dr. Umberto Tassinari are acknowledged for their technical support.

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