The Ability of Soil Pore Network Metrics to Predict Redox Dynamics Is Scale Dependent
Abstract
:1. Introduction
- (a)
- Oxygen diffusion within aggregate domains can be estimated based on porosity alone;
- (b)
- there is a single critical oxygen concentration at which heterotrophic respiration (the major energy yielding process in soil) ceases in all organisms; and
- (c)
- oxygen consumption is constant throughout “aerobic” aggregate domains.
- (a)
- constrain the size of the soil volume that is “seen” by the tip of a platinum probe; and
- (b)
- find quantitative, numerical indices of soil structure that can be used to test assumptions about causality regarding soil structure—redox state relationships.
- (1).
- Parameterization of soil structure using computed tomography. Diffusive domains and the surrounding spatial void pattern within a given soil volume (i.e., soil structure) are considered as quantifiable through X-ray computed tomography (XCT) (XCT, [29]). Nimmo and Perkins [30] hypothesized that as a pore network was increasingly disturbed, macroporosity would decrease. We assumed that, by manipulating saturation level and manipulating the geometry of the pore network while measuring concomitant changes in electromotive potential in multiple microenvironments, relationships between the XCT quantified pore network and the unique redox state contained within could be determined. In doing so, we aimed to contribute to the development of parameters, procedures, and concepts for the application of XCT to the investigation of structure—functionality relations in soil systems [31];
- (2).
- variation of electromotive potentials in soil microenvironments. Our decision to use Pt-electrode potentials for the identification of biogeochemically distinct soil microsites was based on previous reports that Pt-electrodes are probing the redox state of very small individual volumes in the order of a few cubic millimeters [32,33,34]. To address uncertainties regarding the soil volume “seen” by the Pt-electrode tip, the relationships between virtual (i.e., defined by the settings of the analytical software) sub-sections of the pore network (Volumes of Interest, VoI) and measured electromotive potentials were examined; and
- (3).
- variation in moisture content. To elucidate the relationship(s) between wetting and drying events and the formation of anaerobic conditions we focus on short-term time brackets where moisture conditions change how the resulting variations in redox state are predicted by XCT derived pore network metrics.
2. Materials and Methods
2.1. Experimental Approach
2.1.1. Soil Description and Sample Collection
2.1.2. Set Up and Instrumentation
2.1.3. Experimental Conditions
2.2. Pore Network Quantification Using X-ray Computed Tomography
2.2.1. XCT Theory and Scan Conditions
2.2.2. Image Pre-Processing
- (a)
- The available energy sensed by the Pt-electrode tip represents the state of the soil solution in the pore system connecting the soil surface and the electrode tip. The resulting Volume of Interest (VoI100) was of a cylindrical shape centered around the electrode with a height of approximately 8 cm (minor variations between individual cylinders), a diameter of 4 cm, and an average volume of 100 mL.
- (b)
- The potential sensed represents a more constrained, but still sizable, region right below the electrode tip. This VoI had a diameter of 4 cm and extended 2 cm down from the bottom of the probe tip, resulting in a volume of approximately 25 mL (VoI25).
- (c)
- Testing the suggestion of Fiedler [33] that Pt-electrodes are only sensitive to the conditions in a space of few cubic mm immediately surrounding and connected to the platinum tip, we finally selected a volume of interest surrounding the platinum wire in the fashion of a cylindrical sleeve with a height of 7 mm, an inner diameter of 5 mm, and a wall thickness of 0.84 mm, yielding a volume of 190 mm3 or approximately 0.2 mL (VoI0.2). The dimensions of the inner core were chosen to avoid image artifacts created by the metal of the probe tip. Figure 4 demonstrates how the respective images varied as a function of pore network structure. Representative curves are added to reiterate significant differences in available energy dynamics. For each sub-sampled VoI, the contrast was set using Fiji’s auto brightness/contrast setting. The binary threshold was then set manually by comparing pore edges in four different images to the same pore edges in the corresponding images from the 8-bit image stack prior to thresholding [45]. A 3D median filter of the dimensions, 5 × 5 × 5 pixels, was then applied to each binary stack, which reduced noise, but preserved pore edges [46].
2.2.3. Image Analyses
2.3. Statistics
3. Results and Discussion
3.1. Pt-Electrodes Provide Robust and Reliable Information about Available Energy
3.2. The Pore Network Metric—Pore Network Architecture Relationship Depends on the Observed Soil Volume
3.3. Pore Network Architecture Modifies Available Energy
3.4. Pore Network Metrics Have Differential Power to Explain Available Energy Metrics
3.5. The Explanatory Power of PNMs Depends on Pore Network Architecture
3.6. Utility of Available Energy and Pore Network Metrics
3.7. The Explanatory Power of PNMs Is Greatest for a Small Soil Volume Immediately Surrounding the Electrode Tip
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Metric Number | Pore Network Metric (PNM) | Unit | Metric Description |
---|---|---|---|
1 | Number of branches | Count | The number of slab segments (composites of slab voxels) in a VoI |
2 | Total number of junctions | Count | The total number of voxels in the VoI with more than two neighbor voxels |
3 | Mean branch length | mm | Average length of a branch in the VoI; calculated using all branches in the VoI |
4 | Maximum branch length | mm | Length of the longest branch in the VoI |
5 | Number of triple points | Count/mL | The number of junctions in the VoI with exactly three branches, expressed as a count per unit volume |
6 | Number of quadruple points | Count/mL | The number of junctions in the VoI with exactly three branches, expressed as a count per unit volume. |
7 | Total number of skeletons | Count | Number of individual (non-connected) skeleton (centerline) networks in the VoI |
8 | Number of skeletons with branches >1 | Count | The number of skeleton networks that contain at least one junction and branch |
9 | Mean tortuosity | n/a | Mean convolution of all pores in the VoI. Calculated as the sum of all total branch lengths in the sample divided by the sum of the straight-line distances of all branches in the VoI [50] |
Metric Number | Pore Network Metric (PNM) | Unit | Metric Description |
---|---|---|---|
10 | Image based void volume | mm3 | Volume occupied by an individual pore. Reported as average pore volume for each sample. Calculated by counting the number of voxels contained within a given void |
11 | Void surface area | mm2 | Calculated by fitting a triangular surface mesh (via marching cubes) to the interior of each individual void [51] |
12 | Enclosed void volume | mm3 | Volume of an individual void enclosed by triangular surface mesh (0 if no mesh could be fit) |
13 | Mean pore diameter | mm | Calculated at several points as the diameter of the greatest sphere that fits within the void and which contains the point |
14 | Standard deviation of mean pore diameter | mm | Standard deviation of sphere diameters used in mean pore diameter calculation |
15 | Surface area to volume ratio | mm−1 | Surface area divided by image based void volume |
16 | Total number of individual voids | Count | Number of individual voids identified in the VoI |
17 | Number of individual voids with enclosed volume > 0 | Count | The number of voids to which a triangular surface mesh was fit in the VoI |
18 | Image based porosity | % | Number of void voxels in the VoI divided by the total number of voxels in the VoI |
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Wanzek, T.; Keiluweit, M.; Varga, T.; Lindsley, A.; Nico, P.S.; Fendorf, S.; Kleber, M. The Ability of Soil Pore Network Metrics to Predict Redox Dynamics Is Scale Dependent. Soil Syst. 2018, 2, 66. https://doi.org/10.3390/soilsystems2040066
Wanzek T, Keiluweit M, Varga T, Lindsley A, Nico PS, Fendorf S, Kleber M. The Ability of Soil Pore Network Metrics to Predict Redox Dynamics Is Scale Dependent. Soil Systems. 2018; 2(4):66. https://doi.org/10.3390/soilsystems2040066
Chicago/Turabian StyleWanzek, Thomas, Marco Keiluweit, Tamas Varga, Adam Lindsley, Peter S. Nico, Scott Fendorf, and Markus Kleber. 2018. "The Ability of Soil Pore Network Metrics to Predict Redox Dynamics Is Scale Dependent" Soil Systems 2, no. 4: 66. https://doi.org/10.3390/soilsystems2040066
APA StyleWanzek, T., Keiluweit, M., Varga, T., Lindsley, A., Nico, P. S., Fendorf, S., & Kleber, M. (2018). The Ability of Soil Pore Network Metrics to Predict Redox Dynamics Is Scale Dependent. Soil Systems, 2(4), 66. https://doi.org/10.3390/soilsystems2040066