Soil particle density (ρs) is often assumed as 2.65 g cm−3 (density of quartz). The objectives of this study were to compare the use of different gases for determining ρs in a gas pycnometer and relate measured ρs to soil particle size distributions. The ρs of 36 natural soil samples representing 12 USDA textural classes, fine glass beads, crushed granite rock, kaolinite, and bentonite were measured by a commercial gas pycnometer using He, N2, and dry air. The ρs of 30 of the soil samples, glass beads, and crushed rock were also determined with a water pycnometer. The ρs of 36 soil samples determined by He and 30 samples determined by water had narrow ranges with averages of 2.65 and 2.59 g cm−3, respectively. The ρs determined by air and N2 had much wider ranges with averages of 2.93 and 2.97 g cm−3, respectively. There was a near 1:1 relationship between ρs of all samples determined by air and N2 with a highly significant (p < .001) correlation coefficient (r = .99). The average ratio of ρs determined by He and water was 1.03, but the correlation coefficient for their relationship was only .416. Although the relationship between ρs determined by He and either air or N2 was relatively strong (r < .61), the regression coefficient was <.17. There was a strong relationship between soil clay content and ρs determined by N2 or air but a weaker, yet statistically significant (p < .05) relationship when using He. 

Comparison of Soil Particle Density Determined by a Gas Pycnometer Using Helium, Nitrogen and Air

Introduction: Rising industrial emissions of carbon dioxide and methane highlight the important role of carbon sinks and sources in fast-changing northern landscapes. Northern lake systems play a key role in regulating organic carbon input by accumulating carbon in their sediment. Here we look at the lake history of 28 lakes (between 50°N and 80°N) over the past 21,000 years to explore the relationship between carbon accumulation in lakes and temperature changes. Method: For this study, we calculated organic carbon accumulation rates (OCAR) using measured and newly generated organic carbon and dry bulk density data. To estimate new data, we used and evaluated seven different regression techniques in addition to a log-linear model as our base model. We also used combined age-depth modeling to derive sedimentation rates and the TraCE-21ka climate reanalysis dataset to understand temperature development since the Last Glacial Maximum. We determined correlation between temperature and OCAR by using four different correlation coefficients. Results: In our data collection, we found a slightly positive association between OCAR and temperature. OCAR values peaked during warm periods Bølling Allerød (38.07 g·m −2 ·yr −1 ) and the Early Holocene (40.68 g·m −2 ·yr −1 ), while lowest values occurred during the cold phases of Last Glacial Maximum (9.47 g·m −2 ·yr −1 ) and Last Deglaciation (10.53 g·m −2 ·yr −1 ). However, high temperatures did not directly lead to high OCAR values. Discussion: We assume that rapid warming events lead to high carbon accumulation in lakes, but as warming progresses, this effect appears to change as increased microbial activity triggers greater outgassing. Despite the complexity of environmental forcing mechanisms affecting individual lake systems, our study showed statistical significance between measured OCAR and modelled paleotemperature for 11 out of 28 lakes. We concluded that air temperature alone appears to drive the carbon accumulation in lakes. We expected that other factors (catchment vegetation, permafrost, and lake characteristics) would influence accumulation rates, but could not discover a conclusive factor that had a statistical significant impact. More data available on long-term records from northern lake systems could lead to more confidence and accuracy on the matter. 

Effect of temperature on carbon accumulation in northern lake systems over the past 21,000 years

Abstract Particle density ( ρ s ) and bulk density ( ρ b ) are key factors in the calculation of total soil porosity. However, direct measurements of ρ s and ρ b are labour‐intensive, time‐consuming, and sometimes impractical. Pedotransfer functions (PTFs) provide alternative methods for indirect estimation of ρ s and ρ b . In this paper, the accuracy of typical 12 ρ s and 9 ρ b PTFs was evaluated using easily measurable soil properties (sand, silt, clay, and soil organic matter (SOM) content) from granitic residual soils collected from six study areas in subtropical China, and the accuracy of PTFs constructed based on multiple linear stepwise regression (MSR) and machine‐learned algorithms (backpropagation neural network, k ‐nearest neighbour algorithms, random forests, support vector machines, and gradient boosted decision trees) was compared to determine the accuracy of PTFs. The results show that typical PTFs have poor accuracy ( R 2 adjusted &lt; 0.020) and are not applicable to the indirect estimation of ρ s and ρ b in granitic residual soils. The PTFs constructed by machine learning algorithms all performed better than MSR, with the highest estimation accuracy of the PTFs constructed by the random forest algorithm, with R 2 adjusted values of 0.923 and 0.933 for the ρ s and ρ b PTFs, respectively, and root‐mean‐square error of 0.020 g·cm −3 and 0.023 g·cm −3 , respectively. Compared with MSR, the random forest algorithm has greater accuracy and eliminates the restriction of PTFs on predictors, which provides support for understanding the changing rules of ρ s and ρ b in granite residual soils in subtropical regions, evaluating soil quality and improving soil structure. 

A method for estimating the bulk density and particle density of granite residual soil based on the construction of pedotransfer functions

Abstract Soil organic matter (SOM) plays key roles in sloping land erosion control. This study explores SOM content across Hubei Province, China, focusing on four soil types under various conservation strategies. Field samples ( n = 243) were collected under 27 monitoring sites employing diverse conservation strategies in runoff plots. Results indicated substantial variability in SOM content among soil types, with calcareous soils exhibiting the highest levels (12.63 g kg −1 ). Conversely, red soils displayed the lowest SOM content (6.32 g kg −1 ). However, short‐term conservation strategies and their interaction with soil type did not significantly influence SOM. The findings underscore the intricate relationship between soil types and SOM dynamics. This study contributes to the understanding of SOM dynamics in diverse landscapes, offering valuable guidance for policymakers and land managers to apply practices in mitigating erosion and enhancing soil health. 

Soil organic matter characteristics of four soil types under different conservation strategies across Hubei Province

Modern times have required studies that take into account the main soil features, aligning the use of land with the protection of more sensitive environments. From this perspective, this study aimed to perform a morphological description and determine the physical and chemical attributes for soil classification in the community of Poção, located in the municipality of Martins/RN, by highlighting the more sensitive attributes in the differentiation of environments through multivariate analysis. Nine soil profiles were identified to perform the morphological description and collect samples for physical and chemical analyses. The study updates the soil classes found in the study area: Acrisols, Planosols, and Cambisols, with the relief being the main factor responsible for the difference between soil attributes. The influence of organic matter on the soil attributes highlights the importance of its maintenance. Aluminum and the clay fraction are responsible for the distinction of the Acrisol class, whereas silt, potassium sodium, total organic carbon, the electrical conductivity of the saturation extract, and the cation exchange capacity allow the differentiation of Fluvisols.

Characterization and Classification of Soils in Agroecosystems in a Moist Enclave in Northeastern Brazil

Evaluation of pedotransfer functions for predicting particle density of soils with low organic matter contents

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ceng Yi

Author Tools

Chat about Author

Papers

Evaluation of pedotransfer functions for predicting particle density of soils with low organic matter contents