scholarly journals A procedure of determining carbon-13 composition in soil organic carbon on an Isotope Ratio Mass-Spectrometer

2021 ◽  
Vol 8 (1) ◽  
pp. 23-28
Author(s):  
Hong Thinh Nguyen Thi ◽  
Hoai Vu ◽  
Lan Anh Ha ◽  
Van Giap Trinh ◽  
Van Vuong Nguyen
Keyword(s):  
Carbon Sequestration ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Mass Spectrometer ◽  
Isotope Ratio ◽  
Isotope Composition ◽  
Soil Samples ◽  
13C Isotope ◽  
Isotope Ratio Mass Spectrometer ◽  
Good Repeatability

In this study, a procedure of determining the 13C isotope composition ([13C]/[12C]) in soil organic carbon (SOC) using an isotope ratio mass spectrometer (IRMS) was developed. The procedure would be a useful approach in the studies on carbon sequestration that is of great concern among environmentalists worldwide nowadays. The procedure includes: drying, crushing, sifting and removing carbonate in soil samples before the analysis on the mass spectrometer. Results showed that the developed procedure gained a good repeatability of 0.21 ‰. The accuracy of the procedure waschecked by analyzing a surrogate soil sample, a mixture of soil with known δ13CSOC and IAEA-CH-3 cellulose standard.

Analysis of the site-specific carbon isotope composition of propane by gas source isotope ratio mass spectrometer

2016 ◽  
Vol 188 ◽  
pp. 58-72 ◽  
Author(s):  
Alison Piasecki ◽  
Alex Sessions ◽  
Michael Lawson ◽  
A.A. Ferreira ◽  
E.V. Santos Neto ◽  
...  
Keyword(s):  
Carbon Isotope ◽  
Mass Spectrometer ◽  
Isotope Ratio ◽  
Isotope Composition ◽  
Carbon Isotope Composition ◽  
Site Specific ◽  
Gas Source ◽  
Isotope Ratio Mass Spectrometer

scholarly journals Gold-coated silver capsule for elemental analyzer-isotope ratio mass spectrometer: Robust against pretreatment of rock material for organic carbon and δ13C analyses

2021 ◽  
Vol 55 (3) ◽  
pp. e1-e8
Author(s):  
Yohei Matsui ◽  
Wataru Fujisaki ◽  
Junji Torimoto ◽  
Keiko Tanaka ◽  
Manabu Nishizawa ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Mass Spectrometer ◽  
Isotope Ratio ◽  
Rock Material ◽  
Isotope Ratio Mass Spectrometer

Carbon Sequestration in the Soils under Different Land uses in Yamunanagar District of Haryana

2013 ◽  
Vol 36 (1) ◽  
pp. 17-22
Author(s):  
M.K. Gupta ◽  
S. Sharma
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Carbon Sequestration ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Samples ◽  
Land Uses ◽  
Dead Biomass ◽  
Organic Carbon Pool ◽  
One Way Anova

Soil Organic Carbon has been ignored since long because it was treated as a dead biomass. After the awareness of climate change, its importance has been recognized worldwide. Therefore, this study was conducted in four land uses viz. Forests, Plantations, Horticulture and Agroforestry in Yamunanagar district of Haryana. Over all, fifty nine numbers of sampling sites (Four hundred and fourteen soil samples) were selected in all land uses from the district. Variation in the number of samples was due to difference in area available under particular land uses. In Yamunanagar district, maximum SOC pool was under Forests (51.05 t ha-1) followed by Plantations (35.32 t ha-1), Horticulture (33.58 t ha-1) and the least was under Agroforestry (29.22 t ha-1). SOC pool under Forests was 44.54 %, 52.03% and 74.71% higher as compared to Plantations, Horticulture and Agroforestry land uses respectively. SOC pool under Plantations was marginally higher as compared to Horticulture (5.18 %) while it was 20.88 % higher in comparison to Agroforestry. Organic carbon pool in the soils under Horticulture land use was 14.92 % higher as compared to soils under Agroforestry land use. When SOC pool under different land uses were tested by one - way ANOVA, it was found that SOC pool under all land uses were significantly different. SOC pool under Forests was statistically significantly different with the SOC pool under Plantation, Agroforestry and Horticulture. Results of one - way ANOVA indicates that SOC pool between the different plantations was significantly different at 0.05 level.

scholarly journals Carbon Sequestration Potential of Urban Green Spaces (PMC Gardens) in Pune City, India

2021 ◽  
pp. 22-38
Author(s):  
R. Shinde Vijayalaxmi ◽  
M. Mahajan Dnyanesh
Keyword(s):  
Carbon Sequestration ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Samples ◽  
Green Spaces ◽  
Total Carbon ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Carbon Sequestration Potential ◽  
Sequestration Potential

Aim: To estimate the Carbon sequestration potential of trees in Urban green spaces of Pune city. Study Design: The methods suggested by Ravindranath and Ostwald were used for measuring the above and belowground biomass and estimation of carbon pool. Random sampling technique was used to collect soil samples. As the study area were one acre and above, each and every tree was sampled for various parameters. The GPS instrument was used for measuring latitude and longitude of each and every tree. Place and Duration of Study: The gardens developed by Pune Municipal Corporation (total   66 having an area one acre and above) Pune, Three years( from January 2015 to December 2015, January 2016 to December 2016, January 2017 to December 2017) Methodology: The gardens having an area one acre and above were selected for the work. Each and every tree is sampled along with its position on ground by using GPS instrument. Sampling of tree includes measuring Height and Girth at breast height (GBH). Later, the parameters like Volume, Mass, Wood density, Above and Below ground biomass, Total biomass and Total carbon were calculated as per the standard methods given by Ravindranath and Ostwald [1] Soil samples were collected randomly from a depth of 30 cm as it is a zone of highest microbial activity. Walkley‐Black Wet Oxidation method was used to find out soil organic carbon. Results: Total amount of above and belowground carbon sequestered was estimated to be 7,00,507.83 tonnes; litter and deadwood 24,904.05, and soil organic carbon 1879.905; and the sum of all were 7,27,291.785 tonnes. The exotic species sequester 2,69,287.4 tonnes and native sequester 80,966.55 tonnes of carbon. The rates of carbon in active markets are US$ 30 (Thirty dollars) per tonne.  Conclusion: Putting a conservative value of US$ 30 per tonne of CO2 locked in these sampled gardens, this carbon sink of about 7,27,291.785 tonnes of CO2 is worth of US $ 21818753.55 or Indian Rs. 1606733011.422/-It will help in Climate mitigation and reducing the carbon footprints of Pune city.

scholarly journals A Two-Stage Approach to the Estimation of High-Resolution Soil Organic Carbon Storage with Good Extension Capability

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 517
Author(s):  
Sunwei Wei ◽  
Zhengyong Zhao ◽  
Qi Yang ◽  
Xiaogang Ding
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Storage ◽  
Guangdong Province ◽  
Soil Samples ◽  
Estimation Accuracy ◽  
Ann Model ◽  
Soil Layers ◽  
Second Stage ◽  
Organic Carbon Storage

Soil organic carbon storage (SOCS) estimation is a crucial branch of the atmospheric–vegetation–soil carbon cycle study under the background of global climate change. SOCS research has increased worldwide. The objective of this study is to develop a two-stage approach with good extension capability to estimate SOCS. In the first stage, an artificial neural network (ANN) model is adopted to estimate SOCS based on 255 soil samples with five soil layers (20 cm increments to 100 cm) in Luoding, Guangdong Province, China. This method is compared with three common methods: The soil type method (STM), ordinary kriging (OK), and radial basis function (RBF) interpolation. In the second stage, a linear model is introduced to capture the regional differences and further improve the estimation accuracy of the Luoding-based ANN model when extending it to Xinxing, Guangdong Province. This is done after assessing the generalizability of the above four methods with 120 soil samples from Xinxing. The results for the first stage show that the ANN model has much better estimation accuracy than STM, OK, and RBF, with the average root mean square error (RMSE) of the five soil layers decreasing by 0.62–0.90 kg·m−2, R2 increasing from 0.54 to 0.65, and the mean absolute error decreasing from 0.32 to 0.42. Moreover, the spatial distribution maps produced by the ANN model are more accurate than those of other methods for describing the overall and local SOCS in detail. The results of the second stage indicate that STM, OK, and RBF have poor generalizability (R2 < 0.1), and the R2 value obtained with ANN method is also 43–56% lower for the five soil layers compared with the estimation accuracy achieved in Luoding. However, the R2 of the linear models built with the 20% soil samples from Xinxing are 0.23–0.29 higher for the five soil layers. Thus, the ANN model is an effective method for accurately estimating SOCS on a regional scale with a small number of field samples. The linear model could easily extend the ANN model to outside areas where the ANN model was originally developed with a better level of accuracy.

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