C. Effects of Soil-Phase and Aqueous-Phase Ionic Strengths

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156



ronmental samples and (1.92 Ϯ 0.26) ϫ 10Ϫ2 µSϪ1 cm for the youngest degraded

environmental samples. The regression values were r 2 Ն 0.923 in both cases.

According to the model, an increase of sorption is observed when the ionic

strength of the aqueous phase increases. The increase of slope “a” for the youngest degraded environmental samples implies a higher salinity effect on K d , in

agreement with the relative increase of polar compounds when age decreases.

A semiempirical model was developed that allows prediction of K d as a function of exposure time, the salinity of the aqueous phase, and the soil’s clay content. The last variable was included because previous studies show an important

dependence of K d on the soil’s clay content [3]. The linear relationship between

the calculated and measured values of K d has a slope equal to 0.994 (r 2 ϭ 0.884);

this value indicates that ln K d can be estimated with an error of less than 6%.

Although the correlation coefficient is relatively poor, it can be considered a good

fit, taking into account the diversity in the environmental conditions and in the

sources and history of the residuals.

To evaluate the sensitivity of the model to variations in the main factors involved in the prediction of K d , Monte Carlo simulation was applied. Data of

soil electrical conductivity C s , soil clay content (wt/wt%), and initial electrical

conductivity of the aqueous phase C i were generated, according to the distributions in Table 8 (five different simulations). C, K 0 , and K d were calculated for

d

oil residuals with spill age equal to 2, 10, and 20 years.

Simulation 1. It is assumed that the aqueous salinity is less than the soil

salinity, a situation that could correspond to rainwater that has increased its salinity during its superficial runoff. Mean values of electrical conductivity have been

assumed for soil salinity, according to regional data. The results are shown in

Figure 9. The values of K d (L kgϪ1 ) are equal to or less than 1000 for 2-year-old

residuals (95%), while only 42% and 15% present these values for 10-year-old



TABLE 8 Assumed Distributions of C i , Soil Clay Content, and C s for

the Monte Carlo Simulations

Simulation



Variable,

distribution

C i , normal

Clay, normal

C s , normal



1

X

σ

X

σ

X

σ



ϭ

ϭ

ϭ

ϭ

ϭ

ϭ



300,

60

50,

15

600,

100



X ϭ mean, σ ϭ standard deviation.



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2

X

σ

X

σ

X

σ



ϭ

ϭ

ϭ

ϭ

ϭ

ϭ



300,

60

50,

15

2500,

800



3

X

σ

X

σ

X

σ



ϭ

ϭ

ϭ

ϭ

ϭ

ϭ



300,

60

10,

5

600,

100



4

X

σ

X

σ

X

σ



ϭ

ϭ

ϭ

ϭ

ϭ

ϭ



300,

60

85,

5

600,

100



5

X

σ

X

σ

X

σ



ϭ

ϭ

ϭ

ϭ

ϭ

ϭ



500,

50

50,

15

600,

100



Interaction of Oil Residues in Patagonian Soil



157



FIG. 9 Histograms showing results for Simulation 1 (f %: percent frequency).



and 20-year-old residuals, respectively. When the age of the spill increases, the

maximum frequencies shift to higher values of K d .

Simulation 2. A higher electrical conductivity for the soil has been assumed;

the results are shown in Figure 10. When soil salinity is greater than aqueous

salinity, K d (L kgϪ1 ) increases and the maximum frequencies appear at 1500 Յ

K d Յ 3000, for all samples. Therefore, the age of the spill is a secondary factor,

and the values of K d would be affected mainly by soil salinity.



FIG. 10 Histograms showing results for Simulation 2 ( f %: percent frequency).



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Nudelman and Rıos



158



FIG. 11



Histograms showing results for Simulation 3 ( f %: percent frequency).



Simulation 3. The results are shown in Figure 11. In this case, the assumed

mean value and standard deviation for C s correspond to regional sand-clay soils.

The results given in Figure 11 show a decrease of K d , due to the small soil clay

content, and a marked effect of age.

Simulation 4. We have assumed a C s mean value and standard deviation

corresponding to regional clay soils. The results given in Figure 12 show an

increase in K d , due to the high soil clay content: A bigger dispersion of the distribution values as a function of age is observed. These results, together with those



FIG. 12



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Histograms showing results for Simulation 4 ( f %: percent frequency).



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Interaction of Oil Residues in Patagonian Soil



159



FIG. 13 Histograms showing results for Simulation 5 ( f %: percent frequency).



of Simulation 3, are consistent with the new sorption model proposed involving

the oil–clay interaction.

Simulation 5. For the initial aqueous-phase salinity, a high mean value and

standard deviation of C i have been assumed, a situation that could correspond

to oil residuals that are accompanied by water spills, which are extracted together

with the oil and, frequently, have salinity similar to seawater. The results are

shown in Figure 13. When the initial aqueous salinity is greater than the soil

salinity, a decrease in K d is observed: 300 Յ K d Յ 1200 for all samples.

The increase in K d with increasing soil salinity (Simulation 2) would imply

a high degree of oil sorption under these conditions. This would agree with the

observation that, when soil salinity increases, the salinity of the equilibrium aqueous phase also increases and therefore that oil solubility decreases. On the other

hand, this effect is more important than age. This same conclusion arises from the

observation of a decrease in K d when the initial aqueous-phase salinity increases

(Simulation 5). Under these conditions, equilibrium aqueous-phase salinity decreases (due to the adsorption of ions by soil), which would imply an increase

in oil solubility in relation to Simulation 1.

An increase in K d has been observed when increasing the age of the residual

in all of the simulations. However, the equilibrium aqueous-phase salinity minimizes this effect, while the clay content makes the differences more evident (Simulations 3 and 4). This is in agreement with our recent observations that the

increase in K d with the clay content could, in principle, be attributed to an increase

in the sorption area. Nevertheless, since a differential uptake is observed for the

different fractions, this is an indication of strong specific interactions between

polar components of the sorbate and the clay, consistent with the sorption model

proposed [3].



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160



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Nudelman and Rıos



III. PHOTODEGRADATION OF OIL RESIDUALS

UNDER ADVANCED OXIDATIVE PROCESSES

Photooxidation and biodegradation are among the two most important processes

involved in the transformation of crude oil or its products that are released into

a marine environment. Photooxidation affects mainly the aromatic compounds

in crude oil and converts them to polar species; and the susceptibility of crude

oil to biodegradation is increased by its photooxidation [23].

The phenomenon of photodegradation of crude oil via natural sunlight is less

well understood in soil, but may provide an opportunity for the introduction of

novel procedures for the remediation of oil spills. Due to the presence of strong

chromophores and a variety of indigenous reactants in soil, photochemical processes can alter both soil surfaces and the chemicals sorbed to those surfaces.

The heterogeneity of surfaces, however, has not allowed successful modeling of

the photolysis process, as compared to water or air, which offer greater homogeneity. Recent efforts have sought to understand how various factors affect photochemical processes in soil. These include the depth of photolysis, photochemical

quenching-sensitization reactions, and transport processes [15,24,25].

Advanced oxidative processes (AOPs) is the generic name given to a series

of different processes in which OH radicals are the major oxidizing agent. The

most common AOPs are: hydrogen peroxide, ozone, UV/H 2 O 2 , UV/O 3 ,

ferrioxalate/H 2 O 2 , TiO 2 , TiO 2 /H 2 O 2 , TiO 2 /O 3 /UV, and Fenton’s [24–26]. The

photodegradation of oil residuals in Patagonian soils was examined along with

the catalytic effect of some added oxidants. The oil residuals are of different

ages, crude oil sources, and environmental exposure conditions. An artificial sample was also prepared, with crude oil and typical soil (50% clay content), and it

was exposed to the same conditions as the other residuals. The experimental

approach was to expose a series of thin, spiked soil layers (thickness typically

between 0.25 and 2 mm) to a solar light source. The overall disappearance rate

coefficient of the oil, which is generally reported as the photodegradation rate

coefficient, is then determined by measuring the total loss of oil from the soil

layers as a function of time. The selected AOPs in the present work were: H 2 O 2 ,

TiO 2 , Fenton’s, TiO 2 /H 2 O 2 , and TiO 2 /Fenton’s.

All these photodegradation catalysts exhibit a similar pattern: a relatively rapid

decrease in part of the contaminants (fast kinetic), followed by a much slower

decrease in the remainder (slow kinetic). The data could be fitted by a nonlinear

equation (11), with first-order constants for both kinetics, where C t /C 0 is the

fraction of oil remained at t days of the exposure time, C t is the oil concentration

at t, C 0 is the initial concentration, f is the fraction of the oil that is fast degraded,

and k 1 and k 2 are the kinetic constants, for global first-order processes. A similar

model was recently applied to a kinetic desorption of the contaminated soils

[10].



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Interaction of Oil Residues in Patagonian Soil



161



TABLE 9 Experimental Parameters for Eq. (11)

Oil spill

age

Sample (years)

1

2

3

4

5

6

7



Ͼ10

10

6

3

3

2

—



Without catalyst



With catalyst



f



k F (dayϪ1 )



10 3 k s (dayϪ1 )



f



k F (dayϪ1 )



10 3 k s (dayϪ1 )



0.063

0.159

0.156

0.135

0.198

0.051

0.170



0.09

0.043

0.10

0.09

0.13

0.13

0.08



3.9

0.1

0.8

0.4

0.02

0.4

0.9



0.086

0.135

0.086

0.159

0.246

0.136

0.166



0.11

0.06

0.08

0.04

0.07

0.14

0.19



3.2

1.0

1.9

1.4

0.2

0.9

1.6



Ct

ϭ f exp (Ϫk F t) ϩ (1 Ϫ f ) exp (Ϫk S t)

C0



(11)



The experimental parameters ( f, k F , and k S ) are summarized in Table 9 for

the degraded environmental samples (1–6) and for an artificial sample (7), without catalyst and with catalyst. Figure 14 shows the experimental data for oil

residual in soil with 10 years of exposure time. The results indicate that only the

slow kinetics could correspond to a photodegradative process, because only it is

affected by the AOP (TiO 2 /Fenton’s) catalysis. Figure 15 shows that, in the case

of oil residual with two years of exposure time, it is probable that both kinetics

could be affected by AOP (TiO 2 /H 2 O 2 ) catalysis. This is in agreement with our



FIG. 14 C t /C 0 (%), the fraction percentage of oil remaining as a function of sunlight

exposure time (days) for sample 2: without catalyst (circles) and with TiO 2 /Fenton’s catalyst (triangles).



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