Spatial variation and environmental assessment of soil organic carbon isotopes for tracing sources in a typical contaminated site

2017 ◽  
Vol 175 ◽  
pp. 11-17 ◽  
Author(s):  
Qingjun Guo ◽  
Guangxu Zhu ◽  
Tongbin Chen ◽  
Jun Yang ◽  
Junxing Yang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variation ◽  
Carbon Isotopes ◽  
Environmental Assessment ◽  
Contaminated Site ◽  
Organic Carbon Isotopes

scholarly journals Temperature exerts no influence on organic matter <i>δ</i><sup>13</sup>C of surface soil along the 400 mm isopleth of mean annual precipitation in China

2016 ◽  
Vol 13 (17) ◽  
pp. 5057-5064 ◽  
Author(s):  
Yufu Jia ◽  
Guoan Wang ◽  
Qiqi Tan ◽  
Zixun Chen
Keyword(s):  
Environmental Factors ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Isotopes ◽  
Soil Type ◽  
Vegetation Type ◽  
Effect Of Temperature ◽  
Mean Annual Precipitation ◽  
Multiple Regressions ◽  
Organic Carbon Isotopes

Abstract. Soil organic carbon is the largest pool of carbon in the terrestrial ecosystem, and its isotopic composition is affected by a number of factors. However, the influence of environmental factors, especially temperature, on soil organic carbon isotope values (δ13CSOM) is poorly constrained. This impedes the application of the variability of organic carbon isotopes to reconstructions of paleoclimate, paleoecology, and global carbon cycling. Given the considerable temperature gradient along the 400 mm isohyet (isopleth of mean annual precipitation – MAP) in China, this isohyet provides ideal experimental sites for studying the influence of temperature on soil organic carbon isotopes. In this study, the effect of temperature on surface soil δ13C was assessed by a comprehensive investigation of 27 sites across a temperature gradient along the isohyet. Results demonstrate that temperature does not play a role in soil δ13C. This suggests that organic carbon isotopes in sediments cannot be used for paleotemperature reconstruction and that the effect of temperature on organic carbon isotopes can be neglected in the reconstruction of paleoclimate and paleovegetation. Multiple regressions with MAT (mean annual temperature), MAP, altitude, latitude, and longitude as independent variables and δ13CSOM as the dependent variable show that these five environmental factors together account for only 9 % of soil δ13C variance. However, one-way ANOVA analyses suggest that soil type and vegetation type are significant factors influencing soil δ13C. Multiple regressions, in which the five aforementioned environmental factors were taken as quantitative variables, and vegetation type, soil type based on the Chinese Soil Taxonomy, and World Reference Base (WRB) soil type were separately used as dummy variables, show that 36.2, 37.4, and 29.7 %, respectively, of the variability in soil δ13C are explained. Compared to the multiple regressions in which only quantitative environmental variables were introduced, the multiple regressions in which soil and vegetation were also introduced explain more of the isotopic variance, suggesting that soil type and vegetation type exert a significant influence on δ13CSOM.

Spatial variation in soil organic carbon and nitrogen at two field sites under crop and pasture rotations in southern New South Wales, Australia

Soil Research ◽  
2018 ◽  
Vol 56 (8) ◽  
pp. 780 ◽  
Author(s):  
Mark Conyers ◽  
Beverley Orchard ◽  
Susan Orgill ◽  
Albert Oates ◽  
Graeme Poile ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variation ◽  
New South ◽  
New South Wales ◽  
Wagga Wagga ◽  
Soil Cores ◽  
South Wales ◽  
Sampling Protocols ◽  
The Mean

Estimating the likely variance in soil organic carbon (OC) at the scale of farm fields or smaller monitoring areas is necessary for developing sampling protocols that allow temporal change to be detected. Given the relatively low anticipated soil OC sequestration rates (&lt;0.5 Mg/ha.0.30 m/year) for dryland agriculture it is important that sampling strategies are designed to reduce any cumulative errors associated with measuring soil OC. The first purpose of this study was to evaluate the spatial variation in soil OC and nitrogen (N), in soil layers to 1.50 m depth at two monitoring sites (Wagga Wagga and Yerong Creek, 0.5 ha each) in southern New South Wales, Australia, where crop and pasture rotations are practiced. Four variogram models were tested (linear, spherical, Gaussian and exponential); however, no single model dominated across sites or depths for OC or N. At both sites, the range was smallest in surface soil, and on a scale suggesting that sowing rows (stubble) may dominate the pattern of spatial dependence, whereas the longer ranges appeared to be associated with horizon boundaries. The second purpose of the study was to obtain an estimate of the population mean with 1%, 5% and 10% levels of precision using the calculated variance. The number of soil cores required for a 1% precision in estimation of the mean soil OC or N was impractical at most depths (&gt;500 per ha). About 30 soil cores per composite sample to 1.50 m depth, each core being at least 10 m apart, would ensure at least an average of 10% precision in the estimation of the mean soil OC at these two sites, which represent the agriculture of the region.

Spatial variability of soil organic carbon in a typical watershed in the source area of the middle Dan River, China

Soil Research ◽  
2013 ◽  
Vol 51 (1) ◽  
pp. 41 ◽  
Author(s):  
Guo-Ce Xu ◽  
Zhan-Bin Li ◽  
Peng Li ◽  
Ke-Xin Lu ◽  
Yun Wang
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variability ◽  
Spatial Heterogeneity ◽  
Spatial Variation ◽  
Soil Depth ◽  
Source Area ◽  
Soil Layers ◽  
Land Use Types

Soil organic carbon (SOC) plays an important role in maintaining and improving soil fertility and quality, in addition to mitigating climate change. Understanding SOC spatial variability is fundamental for describing soil resources and predicting SOC. In this study, SOC content and SOC mass were estimated based on a soil survey of a small watershed in the Dan River, China. The spatial heterogeneity of SOC distribution and the impacts of land-use types, elevation, slope, and aspect on SOC were also assessed. Field sampling was carried out based on a 100 m by 100 m grid system overlaid on the topographic map of the study area, and samples were collected in three soil layers to a depth of 40 cm. In total, 222 sites were sampled and 629 soil samples were collected. The results showed that classical kriging could successfully interpolate SOC content in the watershed. Contents of SOC showed strong spatial heterogeneity based on the values of the coefficient of variation and the nugget ratio, and this was attributed largely to the type of land use. The range of the semi-variograms increased with increasing soil depth. The SOC content in the soil profile decreased as soil depth increased, and there were significant (P < 0.01) differences among the three soil layers. Land use had a great impact on the SOC content. ANOVA indicated that the spatial variation of SOC contents under different land use types was significant (P < 0.05). The SOC mass of different land-use types followed the order grassland > forestland > cropland. Mean SOC masses of grassland, forestland, and cropland at a depth of 0–40 cm were 5.87, 5.61, and 5.07 kg m–2, respectively. The spatial variation of SOC masses under different land-use types was significant (P < 0.05). ANOVA also showed significant (P < 0.05) impact of aspect on SOC mass in soil at 0–40 cm. Soil bulk density played an important role in the assessment of SOC mass. In conclusion, carbon in soils in the source area of the middle Dan River would increase with conversion from agricultural land to forest or grassland.

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