Quantifying bulk density of boreal peat using X-ray computed tomography

Conversion Conversion and drainage of peat land stimulate soil organic matter (SOM) mineralization, which substantially increase carbon loss from soils. Carbon losses from peat lands are probably a major component in global greenhouse gas emissions. The objectives of this study are to evaluate carbon loss from several land use of peat drained, and to evaluate factors affected carbon loss from several land use on peat drained. The study was conducted in Nanggroe Aceh Darussalam Province from May 2008 until October 2009. Carbon losses were calculated by interpretation data of bulk density (BD), ash content and carbon content from 0-50 cm top soil of peat lands. Peat lands characteristics i.e. physical, chemical and biological properties were investigated by field observation and analysis of peat soil samples on the laboratorium. The results showed that: 1) ash content and bulk density of the peat are related, indicating the partial lost of soil C during decomposition and compaction, 2) an “internal tracer” estimate of peat C loss yields estimates of CO2 flux up to 56 t CO2-eq ha-1 year-1 for young oil palm, highly correlated with the measured rates of subsidence of the surface, 3) landscape level variation in maximum water table, salinity and Fe of peat are correlated with measured peat carbon loss.

Knowledge about the bulk density and porosity of peat and other organic soils is of major importance, as both parameters directly or indirectly effect hydrological conditions (e.g., soil moisture, water level fluctuation), soil physical processes (e.g., shrinkage, swelling, subsidence) and biological processes (e.g., peat mineralization, peat growth). The agricultural usability (e.g., trafficability, plant growth, yield) and the rewetting and oscillation capacity of peatlands also strongly depend on soil hydraulic properties and, thus, on bulk density and porosity. Additionally, knowledge of bulk density is necessary to convert concentrations (e.g., soil organic carbon content) into volume-related quantities. Bulk density and porosity depend on the botanical origin of the peat, the degree of decomposition and other pedogenetic processes. These soil characteristics can be identified directly during soil examinations in the field. In contrast, the determination of bulk density and porosity requires volume-based sampling and subsequent laboratory analyses.Here, we present pedotransfer functions for peat and other organic soils to derive bulk density and porosity using random forest models. Based on a dataset from approximately 600 horizons from 100 peatland sites in Germany and other European countries, we built a set of different pedotransfer functions combining predictor variables determined in the field. These included the degree of decomposition, peat type (e.g., Sphagnum peat, Carex peat, amorphous peat), horizon characteristics (e.g., aggregated, oxidized, permanently saturated, ploughed), average horizon depth, rooting intensity (no roots to extremely dense, estimated from root proportion per cm²), admixture of mineral compounds and the occurrence of carbonate (estimated using 10% hydrochloric acid). Further pedotransfer functions were built, using soil organic carbon content as an additional predictor variable.The results show that bulk density and porosity can be predicted using only a few predictor variables (3-7) with a low bias and high coefficient of determination. Adding soil organic carbon content as an additional predictor variable further improved the pedotransfer functions. Depending on the combination of the predictor variables, root mean square errors (5-fold cross validation) varied between 0.069 to 0.099 g cm-3 for the bulk density and 3.8 to 4.7% for the porosity pedotransfer functions.

Non-destructive measurements of contaminated soil core samples are desirable prior to destructive measurements because they allow obtaining gross information from the core samples without touching harmful chemical species. Medical X-ray computed tomography (CT) and time-domain low-field nuclear magnetic resonance (NMR) relaxometry were applied to non-destructive measurements of sandy soil core samples from a real site contaminated with heavy oil. The medical CT visualized the spatial distribution of the bulk density averaged over the voxel of 0.31 × 0.31 × 2 mm3. The obtained CT images clearly showed an increase in the bulk density with increasing depth. Coupled analysis with in situ time-domain reflectometry logging suggests that this increase is derived from an increase in the water volume fraction of soils with depth (i.e., unsaturated to saturated transition). This was confirmed by supplementary analysis using high-resolution micro-focus X-ray CT at a resolution of ∼10 μm, which directly imaged the increase in pore water with depth. NMR transverse relaxation waveforms of protons were acquired non-destructively at 2.7 MHz by the Carr–Purcell–Meiboom–Gill (CPMG) pulse sequence. The nature of viscous petroleum molecules having short transverse relaxation times (T2) compared to water molecules enabled us to distinguish the water-saturated portion from the oil-contaminated portion in the core sample using an M 0–T2 plot, where M 0 is the initial amplitude of the CPMG signal. The present study demonstrates that non-destructive core measurements by medical X-ray CT and low-field NMR provide information on the groundwater saturation level and oil-contaminated intervals, which is useful for constructing an adequate plan for subsequent destructive laboratory measurements of cores.

Cartilage surface integrity influences contrast enhanced ct imaging of osteoarthritis

Effects of distance from canal and degradation history on peat bulk density in a degraded tropical peatland

Characterizing the immiscible transport properties of diesel and water in peat soil

National and international climate change mitigation plans require a knowledge of peat soil extent across large geographic areas. Peat soils, which play a vital role in carbon storage and climate regulation, have a physical margin where soils change from high to low organic content. Accurate delineation of both national extent of peat soils and peat to mineral soil transition is required for assessing land use and planning effective conservation and carbon loss mitigation strategies. This abstract presents a novel approach for defining both peat soil extent nationally and transition zones between peat and mineral soils at field scale. At a national scale, peat soil maps are created using optical satellite remote sensing or legacy soil/quaternary maps or a combination of both. However, optical remote sensing cannot detect peatlands under landcover such as forest or grassland and legacy maps are often created from sparse in-situ auger data making the accurate delineation of the boundary between peat and mineral soils difficult and cost prohibitive. Airborne radiometric data, which measures natural environmental radiation, has been shown to differentiate between peat and mineral soils due to high attenuation of gamma rays in organic soils. Radiometric data is considered a direct measurement of the subsurface and so is minimally affected by landcover. Additionally, as airborne radiometric data can be acquired in a spatially consistent manner, it has the potential to identify areas of peat soil across the landscape and highlight areas of transition between high and low organic soils. In Ireland, the Tellus survey, acquired by Geological Survey Ireland (GSI) aims to acquire airborne data (including radiometric data), consistently across the country (flight line spacing of 200m) at a resolution of 50 x 50 m. Utilising this national radiometric dataset, a machine learning classification methodology is presented. Data are classified as peat (> 30 % organic material) or non-peat, with 85 % accuracy, is validated using a national soils sampling survey. A confidence value is extracted, once data are classified, which results in the identification peat soils. Several field sites across the midlands of Ireland, which are located at verified transition zones, are then used to show the effectiveness of the classification at identifying transition zones at the field scale. The methodology is robust and can be applied in all areas where these data exist. The results highlight that inclusion of an airborne radiometric dataset in a national climate plan can be used to update national and international carbon inventories of peatlands areas and inform European policy. Understanding the location of these peat to mineral soil transitions is paramount when considering the impact on climate change mitigation strategies such as potential impact of rewetting of peat soils.

Water is an essential factor in forming, utilization, management, and sustainability of peat soil. This study was to obtain characteristics of water retention and porosity of peat soil. Peat samples were taken from the Natural Laboratory of Peat Forest, Central Kalimantan at shallow, medium, and deep peat at 0-50cm (surface) and 50-100 cm (subsurface), while laboratory analyses carried out at Soil Laboratory, Universitas Gajahmada. The result shows that volumetric moisture content at the surface lower than subsurface, except for deep peat. The total pore for the surface was 84.67-86.98%, while subsurface layers were 83.53-86.93%. For surface layer, saturated degree (S) medium peat higher than shallow and deep peat, while for shallow subsurface peat higher than medium and deep peat. S value all pF levels of surface for medium and deep peat higher than the subsurface. Bulk density for surface was 0.094g.cm-3 (rb(wet)) and 0.22g.cm-3(rb (dry)) for shallow peat while medium peat are 0.084–0.087g.cm-3(rb(wet)) and 0.18–0.20g.cm-3(rb(dry)), deep peat 0.064–0.090g.cm-3(rb(wet)) and 0.11–0.16g.cm-3(rb(dry)). For subsurface, bulk density of medium peat are 0.094–0.107g.cm-3 (rb(wet)) and 0.16–0.20g.cm-3 (rb(dry)), deep peat are 0.067–0.090g.cm-3 (rb(wet)) and 0.10–0.17g.cm-3 (rb(wet)). The particle density of surface and subsurface for shallow peat higher than medium and deep peat, with values 0.67-0.77g.cm3, 0.61-0.66g.cm3, and 0.53-0.63g.cm3 for shallow, medium, and deep peat, respectively. Total pores for the surface layer decrease with increasing dry bulk density (R = 0.624) and particle density (R = 0.375). This fact seems to confirm a directly proportional relationship between parameters bulk and particle density with total pores.

ABSTRACTMeasuring soil bulk density (as well as carbon content) is crucial for accurate soil carbon stock calculations. Given the growing interest in soil carbon sequestration on farmlands as a strategy for mitigating greenhouse gas emissions, effective large‐scale field monitoring has become more important than ever. However, traditional methods for measuring soil bulk density, such as the core (volumetric cylinder) and clod methods, require undisturbed samples, making them labour‐intensive, time‐consuming and costly—due to the high complexity of sample collection and preparation. To overcome these challenges, we developed a laser‐induced breakdown spectroscopy (LIBS)‐based method for efficient and cost‐effective bulk density estimation that does not require undisturbed samples. We trained and evaluated LIBS‐based models using a dataset of 880 diverse Brazilian soil samples, randomly split into 70% for training and 30% for testing. The LIBS‐based models, combining discrete wavelet transform (DWT), feature selection via F‐test for regression, and Ridge regression, achieved an R2 of 0.72 and a root mean square error (RMSE) of 0.12 g cm−3 on the test set for soil bulk density prediction. Furthermore, by combining LIBS‐predicted soil bulk density with measured soil carbon concentration, we estimated soil carbon stock, achieving an R2 of 0.93 and an RMSE of 2.2 Mg C ha−1 on the test set, indicating that the uncertainty in bulk density predictions has a minor impact on soil carbon stock estimations. To further streamline soil carbon stock estimation, we developed a model to directly predict soil carbon density—the product of soil carbon concentration and bulk density—using LIBS‐derived spectral features, eliminating the need for separate measurements or estimations. Although this approach resulted in a lower R2 of 0.78 and a higher RMSE of 4.1 Mg C ha−1, its performance was adequate for carbon stock prediction while simplifying the estimation process. These findings highlight the potential of LIBS as a rapid and effective tool for assessing soil bulk and carbon densities, contributing to sustainable soil management and climate change mitigation and adaptation.

The workflow for an integrated well log analysis must incorporate physically-consistent models for rock properties prediction. Therefore, the selection of the conceptual model is a crucial step in deriving the petrophysical model under investigation. In this paper, we apply the parallel layers conceptual model to derive a petrophysical model for bulk density of complex lithologies. This conceptual model assumes the natural rock as a set of parallel layers with individual densities, incorporating the main factors affecting bulk density of sedimentary formations (i.e., the solid matrix, porosity and fluid content). The resulting petrophysical model shows the volumetric fractions of individual rock constituents as the key parameters for bulk density description. Further parameters of the dependence can be easily selected from petrophysical tables. In this way, evaluation of predefined volumetric fractions of rock constituents is a mandatory procedure for applying the investigated petrophysical model. We present results of calibration and estimation of bulk density well log measurements through turbiditic sediments forming the Namorado reservoir, Campos basin. In evaluating the facies-described volumetric fractions of main constituents of rocks at well surroundings, fundamental well log measurements represented the inputs for mineral volume analysis using the non-negative least-squares inversion method. The outcomes of both experiments exhibited the good performance of the petrophysical model in estimating bulk density with negligible absolute errors and high correlation coefficient. As a conclusion, the parallel layers conceptual model revealed enough robustness for construction of petrophysical models of other well log measurements.

Throughout the world, many extensive wetlands, such as the Sacramento-San Joaquin Delta of California (hereafter, the Delta), have been drained for agriculture, resulting in land-surface subsidence of peat soils. The purpose of this project was to study the in situ effects of wetland drainage on the remaining peat in the Delta. Peat cores were retrieved from four drained, farmed islands and four relatively undisturbed, marsh islands. Core samples were analyzed for bulk density and percent organic carbon. Macrofossils in the peat were dated using radiocarbon age determination. The peat from the farmed islands is highly distinct from marsh island peat. Bulk density of peat from the farmed islands is generally greater than that of the marsh islands at a given organic carbon content. On the farmed islands, increased bulk density, which is an indication of compaction, decreases with depth within the unoxidized peat zone, whereas, on the marsh islands, bulk density is generally constant with depth except near the surface. Approximately 55-80% of the original peat layer on the farmed islands has been lost due to land- surface subsidence. For the center regions of the farmed islands, this translates into an estimated loss of between 2900-5700 metric tons of organic carbon/hectare. Most of the intact peat just below the currently farmed soil layer is over 4000 years old. Peat loss will continue as long as the artificial water table on the farmed islands is held below the land surface.

Carbon accumulation in coastal wetlands is normally assessed by extracting a sediment core and estimating its carbon content and bulk density. Because carbon content and bulk density are functionally related, the latter can be estimated gravimetrically from a section of the core or, alternatively, from the carbon content in the sample using the mixing model equation from soil science. Using sediment samples from La Paz Bay, Mexico, we analyzed the effect that the choice of corer and the method used to estimate bulk density could have on the final estimates of carbon storage in the sediments. We validated the results using a larger dataset of tropical mangroves, and then by Monte Carlo simulation. The choice of corer did not have sizable influence on the final estimates of carbon density. The main factor in selecting a corer is the operational difficulties that each corer may have in different types of sediments. Because of the multiplication of errors in a product of two variables subject to random sampling error, when using gravimetric estimates of bulk density, the dispersion of the data points in the estimation of total carbon density rises rapidly as the amount of carbon in the sediment increases. In contrast, the estimation of total carbon density using only the carbon fraction as a predictor is very precise, especially in sediments rich in organic matter. This method, however, depends critically on the accurate estimation of the two parameters of the mixing model: the bulk density of pure peat and the bulk density of pure mineral sediment. The estimation of carbon densities in peaty sediments can be very imprecise when using gravimetric bulk densities. Estimating carbon density in peaty sediments using only the estimate of organic fraction can be much more precise, provided the model parameters are estimated with accuracy. These results open the door for simplified and precise estimates of carbon dynamics in mangroves and coastal wetlands.

Estimating soil bulk density with information metrics of soil texture

Tropical peatlands are unique wetland ecosystems which provide various ecosystem services such as carbon storage and nutrient cycling. However, they have been substantially altered and transformed by land use conversion. The present study investigated the effects of land use conversion on the physico-chemical properties of peat in the Leyte Sab-a Basin Peatland, a tropical peatland on Leyte Island, Philippines. Peat core samples (1 m long) were collected from peat swamp forest, grassland and cultivation areas. Samples were analysed for gravimetric water content, volumetric water content, dry bulk density, porosity, pH, organic matter, total nitrogen and total phosphorus. Notably, conversion of peat swamp forest to other land uses (grassland and cultivation) has resulted in changes in peat physical properties such as reduced water content and porosity, and increased bulk density. A reduction in peat water content can be a direct consequence of peatland drainage while an increase in peat bulk density with reduced porosity reflects compaction owing to the passage of agricultural equipment and peat decomposition. Land use conversion altered chemical properties characterised by reduced organic matter and nutrients (total nitrogen and total phosphorus) in grassland or cultivation, indicating peat decomposition and mineralisation. In addition, decrease in peat water content due to drainage and increase in bulk density can be accompanied by losses of organic matter and nutrients. Finally, changes in peat physico-chemical properties as a consequence of land use conversion serve as important indicators of peat soil degradation.

Drainage and conversion of natural peatlands, which increases fire frequency, haze air pollution and carbon emissions, also affects the physical and chemical properties of peat soils. Although there has been continued interest in research on tropical peat soil properties, no attempt has yet been made to synthesise these results. We conducted a systematic literature review and meta-analysis of sixty-six papers published in English language academic literature to explore the current state of knowledge of peat soil properties of Southeast Asia and to compare physical and chemical peat properties (e.g. bulk density, carbon content, pH) under different land uses and depths. Most of these studies were undertaken in Indonesia (56.1%) and Malaysia (28.8%), where substantial tracts of peat soils occur. We extracted data from these papers to calculate the mean of each peat property and compare results between land uses and depths. Linear mixed-effects models were used to test the significance of land use and depth on each peat property. We found that bulk density (44 papers), carbon (C) content (43 papers), pH (42 papers) and nitrogen (N) content (39 papers) were the most widely reported, while other properties remain less studied. Bulk density, pH, phosphorus (P) and calcium (Ca) showed significant differences between land uses and depths. Fibre fraction, potassium (K), iron (Fe) and zinc (Zn) levels showed a significant difference between land uses only, while N differed significantly only between soil depths. Other physical properties such as hydraulic conductivity, porosity, woody fraction, amorphic fraction and chemical properties such as electrical conductivity (EC), C, ammonium (NH4+), nitrate (NO3−), available nitrogen (available N), magnesium (Mg), aluminium (Al), copper (Cu), manganese (Mn), sulphur (S) and silicon (Si) showed no significant differences between land uses or depths. This review identifies key research gaps, including underrepresented geographic areas and peat properties and highlights the need for standardised methodologies for measuring peat soil properties.