Soil properties, variability

The results of measured soil property variables for both core locations appear in Figures 1, 2 and 3. Portions of the profiles were not sampled and appear as breaks in the figure columns. 

Fig. 8-3 Diagram illustrating the development of positively and negatively charged sites on surfaces of soil constituents, at low and high pH. (Reproduced with permission from R. L. Parfitt (1980). Chemical properties of variable charge soils. In "Soils with Variable Charge" (B. K. G. Theng, ed.), p. 168. New Zealand Society of Soil Science Offset Publications.)...

Amini M., Khademi H., Afyuni M., Abbaspour K.C. Variability of available cadmium in relation to soil properties and landuse in an arid region in central Iran. Water Air Soil Pollut 2005 162 205-218. 

In watersheds where surface runoff is limited by infiltration rate rather than soil-water storage capacity, areas of the watershed can alternate between sources and sinks of surface flow. This again will be a function of soil properties, rainfall intensity and duration, and antecedent moisture condition. As surface runoff is the main mechanism by which phosphorus is exported from most watersheds, it is clear that, if surface runoff does not occur, phosphorus export can be small. Thus, consideration of hydro-logic pathways and variable source areas is critical to a more detailed understanding of phosphorus export from agricultural watersheds. 

Fig. 8.31 Adsorption isotherms of terbuthylazine and atrazine at different depths along the soil profile, as affected by the vertical variability of soil properties. (Dror et al. 1999)...

Figure 12.4 shows the spatial distribution of terbuthylazine and bromacil concentrations in a vertical cross section, after leaching by successive irrigations. Differences between the concentrations of the two herbicides may be explained in terms of the chemical properties and the soil spatial variability. The bnUc of the terbnthylazine remained close to the surface, being strongly affected by adsorption. In contrast, bromacil has a higher solubility in water and is leached downward under the inflnence of the spatial variability in hydranUc conductivity. 

Parfitt, R.L. Smart, R.S.C. (1977) Infrared spectra from binuclear bridging complexes of sulphate adsorbed on goethite (a-FeOOH). J. Chem. Soc. Faraday Trans. I. 73 796-802 Parfitt, R.L. Smart, R.S.C. (1978) The mechanism of sulfate adsorption on iron oxides. Soil Sci. Soc. Am. J. 42 48-50 Parfitt, R.L. (1980) Chemical properties of variable charge soils. In Theng, B.K. (ed.) Soils with variable charge. N. Z. Soc. Soil Sci., Lower Hutt. N. Z., 167-194 Parfitt, R.L. (1982) Competitive adsorption of phosphate and sulphate on goethite (a-FeOOH) A note. New Zealand J. Sci. 25 147-148... 

Considerable variation in K(k values appear in the literature. For example, Mackay et al. (1992) provided 24 values of K(k for benzene, ranging from 0.11 to 2.08 L/kg. Variation is likely the result of differences in the sorption characteristics of SOM, variation in the methods used to determine K(k (separation of phases, mass balance, single point, or isotherm, kinetics), impact of other soil properties, and the properties of chemicals being sorbed. The variability in Koc values is generally greater for the more polar compounds. 

Thus, triazine movement and persistence are influenced by many factors, the interactions of which are not always easy to predict. Several models have been used as tools to estimate losses and to identify variables that will impact the rate and magnitude of loss. Considering the broad range in soil properties and climatic conditions used, some models performed well. However, modeling results and predictions are only estimates, and the fate and transport of triazines in the soil environment has been shown to be affected by many factors, including concentration, soil texture, variation in climate, and differences in tillage practices. 

Contaminant distribution in soil and water depends on such factors as soil properties the physical and chemical properties of the contaminant contaminant fate and transport in soil, groundwater or surface water and even the manner in which the contaminant was introduced into the environment. The knowledge of these issues coupled with available information on site history and background allows us to make valid assumptions in the planning phase on contaminant distribution and variability at the site. 

The complexity and variability of soil properties work as both an advantage and a hindrance. Complexity means that many different characterizations can be used to provide high-resolution fingerprints, but equally the variability in this complexity creates a problem of ensuring that reference samples are representative and that sampling of real cases accounts for the expected variation. 

When soil parameters influencing the availability of a substance are known and models are available to predict the available fraction, SQSs can be expressed as a function of these soil properties. This approach reduces the variability of the data and increases the ecological relevance of the SQS. 

Soil Immobile hard to dilute exposure, especially to persistent compounds, can be temporally quite constant, but spatially patchy soil properties (pH, organic matter content, cat ion exchange capacity) have large effects on exposure (bioavailability) and can show spatial and temporal variability... 

Structural hierarchy and heterogeneity are intrinsic soil properties that have profound effects on soil functioning in natural and man-made ecosystems. Quantifying soil structural hierarchy and its effects on soil processes is both an imperative and a challenge for soil physicists. Continual advances in this field benchmark soil science history. One of the advances was the introduction of the scaling concept, which showed simple and consistent ways of quantifying inherent soil variability and structural hierarchy (Nielsen et al., 1998). 

A hierarchy of soil parameters was established in the previous section. If the coefficients of variation are high, on the order of 100%, several hundred soil samples would need to be taken to characterize that soil property. Consequently, the variability of the hydraulic conductivity dictates to what extent we can accurately predict pesticide transport. Even within the same soil texture 42-4), coefficients of variation for the hydraulic conductivity were found to be well in excess of 100%. 

One-Dimensional Simulation of DBCP Movement at Kunia. DBCP distribution three years after the pesticide spill was simulated by the one-dimensional analytical model with exponential decay source term at the surface (1.) predicted concentrations are shown in Figure 2. Measured concentrations from Boreholes 2, 3 and 5 are also shown in Figure 2 for comparison with simulated results. The three measured DBCP concentration profiles are quite variable, both in shape and in magnitude of concentrations. The reason for the variation in measured profiles is not known, but may be due to differences in the amount of DBCP which entered the soil at each location and variation in soil properties between borehole sites. There appears to be a correlation between the sorption values in Table 1 for Boreholes 2 and 3 and the retention of DBCP near the surface at these two locations, ie. high sorption in the surface soil at site 3 resulted in high retention of DBCP, in contrast to site 2. 

For dealing with variability of soil properties at the larger scale, a continuum approach is implemented. Thereby a representative elementary volume (REV) is considered to exist and material properties related to flow and transport are defined at the centre of this REV. Thermodynamic principles related to conservation of mass and momentum are further applied on the REV to obtain governing flow and transport equations. The... 

In practice, this local scale is considered to correspond to the size of the characterization techniques of local soil properties, let s say a small laboratory column. As such the microscopic pore scale variability is no longer explicitly modelled but encoded through effective flow and transport properties at the macroscopic level. The effective macroscopic properties contain of course the signature of the lower level microscopic variability. As such macroscopic effective moisture retention function, hydraulic conductivity or hydrodynamic dispersivity is determined by microscropic pore size distribution, connectivity and tortuosity within the macroscopic sample. 

For more complicated descriptions of the variability of the soil properties and of the flow and transport boundary conditions, different numerical methods are used such as finite difference or finite element integration (van Genuchten, M.Th. and Simunek, J. (1996)). The flexibility with which the boundary conditions and the soil variability can be described makes numerical models particularly attractive tools in chemical management. In a simplified form, only 1-D vertical transport in the field is considered and the 1-D forms of eqs. (1) and (11) are numerically integrated. 

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