5.5 Model Assessment and Implications
5.5.3 Assessment of Model Simplifications and Limitations
The model presented in this chapter contains certain simplifications. I focused on the effect of SOM on the kinetics of Zn sorption and desorption, although other soil components, such as clay minerals, may contribute to Zn binding. Since WHAM can be used to predict the distribution of Zn among soil components after partition equilibrium, it is illustrative to show how Zn was distributed under the typical condition of my sorption experiments. Figure 5.8 shows how Zn is distributed among soil components predicted using WHAM VI. The solution Zn concentration was fixed at 1.8 mg/L and all soil components were input using the property of the Boonton Bergen County soil. More detailed of description of this soil properties can be found elsewhere (Impellitteri, 2000). The sorption of Zn on SOM increases dramatically with the increase of solution pH. At pH > 5, the SOM becomes the dominant sorbent and the mineral phases (metal oxides and clay) only bind about 10%
of total bound Zn. This WHAM simulation strongly supports that SOM is the most important phase in soils for Zn binding.
0 0.2 0.4 0.6 0.8 1
4 5 6 7 8
SOM Oxides Clay
Fraction of Zn bound to each soil component
pH
Figure 5.8 Distribution of Zn among soil components for the Boonton Bergen County soil at different pH predicted using WHAM VI. ([Zn] = 1.80 mg/L).
Another assumption built in this model is the linear sorption isotherm for Zn, in which the partition coefficient Kp is constant at different Zn loadings. From the model calculations, the highest particulate Zn concentration after 3-hour sorption is 540 àg/g. It will be very interesting to see how WHAM VI predicts the partition coefficient at different Zn loadings in soils. Figure 5.9 presents how Zn binding by SOC change with the particulate Zn concentrations predicted using WHAM VI at different pH. The SOC concentration was chosen as 2.32%.
From Figure 5.9(a) we can see at low pH the Kp does not vary a lot with Cp, indicating that a linear isotherm may be a good assumption. At high pH, Kp vary about 3 times from low Zn loading to the high Zn loading, which affects the
accuracies of model predictions based on the linear isotherm. Another interesting thing is, even at same range of Zn loading, the nonlinearity of Zn binding is different at different pH. With higher loading, the amount of available sites will be decrease significantly, which is not considered in this model. The sorption isotherm (Figure 5.9(b)) demonstrated Zn binding within a wide range of Zn concentrations. In my kinetics experiment, the Zn concentrations in soils were less than 400 àmol/g OC.
Cheng (2003) investigated Zn complexation by DOM at various experimental conditions. Generally, at low Zn loadings, the Zn titration isotherms were close to linear. A replot of Figure 5.8 in his dissertation (Cheng, 2003) within low Zn concentrations is presented in Figure 5.10. We can see that Zn binding by DOM is linear at low Zn concentrations.
0 2 4 6 8 10 12 14
0 100 200 300 400 500 600 700
pH 5.5 pH 6.0 pH 6.5
K p (L g-1 )
Cp (àg g-1)
(a)
Figure 5.9 WHAM VI predicted Zn sorption behavior at different pH. (a) Equilibrium partition coefficients vs. particulate Zn concentrations;
(b) sorption isotherm. ([SOC] = 2.32%).
0 200 400 600 800 1000
0 500 1000 1500 2000
pH 5.5 pH 6.0 pH 6.5
C p ( à mo l/ g OC )
Cion (àg/L)
(b)
Figure 5.9 Continued.
0 0.1 0.2 0.3 0.4 0.5
0 5 10-7 1 10-6 1.5 10-6 2 10-6 2.5 10-6 3 10-6
pH 6 pH 7 pH 8
Zn L ( m M / g O C )
Zn ion (M)
Figure 5.10 The titration isotherm for Zn complexation by DOM at different pH values.
The competition of other cations is also not considered, so the model parameters obtained are related to the constant Ca concentrations used in this study. It is recognized that Ca can compete with Zn for the same binding sites in soils.
Different cations bindings to soils or humic substance and the competitive effect has been studied (Tipping et al., 1995a; Kinniburgh et al., 1996). Further studies should incorporate the competitive effect of other cations on Zn sorption/desorption kinetics.
The competitive effect can be handled in a similar way as the pH effect (the proton competition) I used in this study, which is independent on the soil organic matter content. Thus I expect the organic carbon normalized rate coefficients can be applied to other conditions when other competition cations were present.
In addition, the short-term Zn sorption and desorption reactions are different from the long-term reactions with aged soils, in which other processes such as diffusion may control the slow reactions (Barrow, 1998). As discussed in Chapter 4, the model parameters obtained in this study are more suitable for the fresh
contaminated soils.
Nevertheless the model presented in this Chapter provides for quantitative calculations for Zn sorption and desorption kinetics at different solution chemistry, soil composition and residence time, which essentially improved the model proposed in Chapter 3 and our understanding on the kinetics reactions of Zn with soils. It can be used to more accurately assess the kinetics effect of Zn and soil reactions in the complex processes happening in the field.
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Chapter 6
DEVELOPMENT OF THE PREDICTIVE MODEL FOR KINETICS OF CU SORPTION/DESORPTION ON SOILS