**6. Conclusion**

for three levels of Cu (non‐contaminated and contaminated system), at three pH levels (covering the most naturally‐occurring pH reactions in different rhizosphere environments) and at three levels of soil dissolved OM (DOC; corresponds for mineral to organic soil types)

**DOC10 DOC20 DOC30 Species1,2 Cu40 Cu250 Cu500 Cu40 Cu250 Cu500 Cu40 Cu250 Cu500**

<sup>+</sup> 0 1 2 0 0 1 0 0 0

2- 0 0 0 0 0 0 0 0 0

<sup>+</sup> 0 0 0 0 0 0 0 0 0

2- 0 0 0 0 0 0 0 0 0

<sup>+</sup> 0 0 0 0 0 0 0 0 0

2- 0 0 2 0 0 0 0 0 0

CuCO3 (aq) 0 0 0 0 0 0 0 0 0 Cu(OH)2 (aq) 0 0 0 0 0 0 0 0 0

HA1‐Cu (6) (aq) 60 61 55 55 67 68 50 66 70 HA2‐Cu (6) (aq) 36 20 14 44 27 21 49 31 25 **pH 7** Cu2+ 0 1 2 0 0 0 0 0 0

CuCO3 (aq) 0 1 4 0 0 1 0 0 0 Cu(OH)2 (aq) 0 0 0 0 0 0 0 0 0

HA1‐Cu (6) (aq) 5 13 18 3 9 13 3 7 11 HA2‐Cu (6) (aq) 95 85 75 97 91 85 97 93 89 **pH 9** Cu2+ 0 0 0 0 0 0 0 0 0

CuCO3 (aq) 0 1 4 0 0 0 0 0 0 Cu(OH)2 (aq) 0 0 1 0 0 0 0 0 0

HA1‐Cu (6) (aq) 0 1 2 0 1 1 0 0 1 HA2‐Cu (6) (aq) 100 97 91 100 99 98 100 99 99

**Table 2.** Distribution (%) of Cu species in tested soil solution, estimated by Visual MINTEQ chemical equilibrium software (NICA‐Donnan model) as affected by soil pH (5, 7, and 9), dissolved organic carbon (DOC; 10, 20, and 30

Even though the total soil Cu content by itself is not an adequate measure to determine Cu mobility and phytoavailability, a strong positive correlation between total element concentra‐

HA‐Cu: humic acid‐complexed Cu via 1‐carboxylic and 2‐phenolic functional groups.

Species with the < 0.5% of total concentration are not shown.

mgL-1) and different soil Cu total concentration (40, 250, and 500 mg kg-1).

**pH 5** Cu2+ 4 18 28 1 6 11 0 3 5

**% of total concentration % of total concentration % of total concentration**

(**Table 2**).

156 Groundwater - Contaminant and Resource Management

CuNO<sup>3</sup>

Cu(CO3)<sup>2</sup>

CuNO<sup>3</sup>

Cu(CO3)<sup>2</sup>

CuNO<sup>3</sup>

Cu(CO3)<sup>2</sup>

1

2

Located at the atmosphere, plant, soil, and water interface, vadose zone is represented by a variety of linked complex processes. For solving problems such as the transport of nutrients, pesticides, pharmaceuticals, colloids, bacteria, viruses, hormones, and toxic trace elements, carbon sequestration, and bioremediation of organic contaminants, a thorough understanding and coupling of multiple hydrogeological, geochemical, and microbiological processes is needed. Models should be considered as one of the most advanced and useful 'tools' which, when used properly, can predict different scenarios, with positive or negative outcome, that can occur in the natural systems. With development of numerical models, such complex problems can be solved more successfully using different mathematical expressions and approaches. The accuracy of model predictions rely largely upon a quality and quantity of input parameters required for a specific problems, mostly due to large heterogeneity of the soil. Thus, fundamental knowledge of basic soil physics is needed, which combined with new measurement techniques provides satisfactory foundation for performing modelling. In recent years, scientists have been mostly engaged in the coupling of different numerical models, since no single model is yet available for describing such complex system as soil vadose zone [59]. Development of coupled numerical models capable of describing unstable preferential flow in soils, as well as models coupled with sophisticated geochemical models capable of describ‐ ing complex kinetic chemical and biological reactions will remain a focus of research in the near future.
