**6. Perspectives for studies related to movement of water and solutes in soils**

The use of non-destructive techniques such as X-ray computed tomography (XCT) is expected to advance studies of water and solute movement in soils. 2D and 3D image analysis allows for more detailed quantification of pore space architecture and understanding of soil functions [72]. In particular, studies using 3D image analysis applied to soil science specifically in soil physics have increased in recent decades. The advantages of the XCT are the quantification of parameters such as connectivity, tortuosity, intrinsic permeability, pore morphology, pore area, pore volume, and pore size distribution [73, 74]. Some parameters are important for predicting the movement of water and solutes in soils and cannot be quantified by conventional analyses such as the soil water retention curve (SWRC) and application of capillary theory [75].

Smet et al. [76] investigated how the pore geometry and connectivity of the airfilled pore space change as soils are progressively dried. Twenty samples of a loamy soil were equilibrated at different matric potentials and scanned by X-ray microtomography. The results showed an increase in connectivity and a decrease in tortuosity as the matric potential was lowered. Pessoa et al. [75] studied the behavior of *SWRC* and *Ksat* in highly weathered soils influenced by parameters obtained from 3D image analysis. It was observed a better connectivity for the biggest pore, confirmed by the more negative values of the Euler Number (ENbigpore). The better-connected pore space increased the *Ksat* and changed the shape of the SWRCs.

Anderson et al. [77] used XCT to evaluate chemical transport into groundwater through undisturbed geomedia samples. The XCT-based methods presented similar dispersivity values, while the flow method presented high values, a behavior related to additional dispersion promoted by the plate at the end of the column. XCT was efficient in predicting solute transport parameters. Recently, Pak et al. [78] have

presented an interesting approach to the technical challenges of performing of XCTbased fluid flow experiments. The authors also discussed the high spatial and temporal resolution that can be achieved with synchrotron imaging sources.

XCT is an important tool to better understand the movement of water and solutes in the soil. However, there are some limitations in the use of the technique, such as strategies to improve image processing (i.e., reconstruction, segmentation, quantification, and simulations), in addition to high computational requirements for processing [79]. Currently, deep learning and machine learning strategies are used for fast and efficient image processing. This type of data analysis allows extracting information from complex datasets [80–82]. Important detailed information on the use of computed tomography applied to soil physics can be found in Mooney et al. [83].
