scholarly journals Geochemical Distribution Characteristics of Rare Earth Elements in Different Soil Profiles in Mun River Basin, Northeast Thailand

2020 ◽  
Vol 12 (2) ◽  
pp. 457 ◽  
Author(s):  
Wenxiang Zhou ◽  
Guilin Han ◽  
Man Liu ◽  
Chao Song ◽  
Xiaoqiang Li
Keyword(s):  
Rare Earth ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Rare Earth Elements ◽  
River Basin ◽  
Soil Ph ◽  
Enrichment Factors ◽  
Soil Profiles ◽  
Distribution Characteristics ◽  
Northeast Thailand

Exploring the distributions of rare earth elements (REEs) in soil profiles is essential to understanding how natural and anthropogenic factors influence the geochemical behaviors of REEs. This study aimed to learn about the distribution characteristics of REEs in soils, including their fractionation and enrichment, and to explore the influence of soil pH and soil organic carbon (SOC) on REEs. One hundred and three samples were collected from six soil profiles under different land uses (paddy field: T1, T3; forest land: T2, T6; wasteland: T4; building site: T5) in the Mun River Basin, Northeast Thailand. The average total REE contents (∑REE) are much lower (<80 mg kg−1) than that of Earth’s crust (153.80 mg kg−1) in soil profiles T2, T3, T4, and T6. The contents of REEs tend to increase slightly with depth in all soil profiles. The ratios of (La/Yb)N range from 0.35 to 0.96 in most samples, indicating that the enrichment of heavy REEs (HREEs) relative to light REEs (LREEs) is the main fractionation pattern. Samples from profile T2 show relatively obvious negative Ce anomalies (0.55–0.78) and positive Eu anomalies (1.41–1.56), but there are almost no anomalies of Ce and Eu in other soil profiles. Enrichment factors of LREEs (EFLREEs) range from 0.23 to 1.54 and EFHREEs range from 0.34 to 2.27, which demonstrates that all soil samples show no LREE enrichment and only parts of samples show minor HREE enrichment. Soil organic carbon (SOC) contents positively correlate with the enrichment factors of REEs (EFREE) in soil profiles T1 (R = 0.56, p < 0.01) and T6 (R = 0.71), while soil pH values correlate well with EFREE in soil profiles T2 (R = 0.75) and T4 (R = −0.66, p < 0.01), indicating the important influence of soil pH and SOC on the mobility of REEs in some soil profiles.

scholarly journals Determining the Distribution and Interaction of Soil Organic Carbon, Nitrogen, pH and Texture in Soil Profiles: A Case Study in the Lancangjiang River Basin, Southwest China

Forests ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 532 ◽  
Author(s):  
Wenxiang Zhou ◽  
Guilin Han ◽  
Man Liu ◽  
Jie Zeng ◽  
Bin Liang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
River Basin ◽  
Soil Ph ◽  
Soil Texture ◽  
Soil Depth ◽  
Soil Samples ◽  
Soil Profiles ◽  
Soil Organic Nitrogen ◽  
Silty Loam

The profile distributions of soil organic carbon (SOC), soil organic nitrogen (SON), soil pH and soil texture were rarely investigated in the Lancangjiang River Basin. This study aims to present the vertical distributions of these soil properties and provide some insights about how they interact with each other in the two typical soil profiles. A total of 56 soil samples were collected from two soil profiles (LCJ S-1, LCJ S-2) in the Lancangjiang River Basin to analyze the profile distributions of SOC and SON and to determine the effects of soil pH and soil texture. Generally, the contents of SOC and SON decreased with increasing soil depth and SOC contents were higher than SON contents (average SOC vs. SON content: 3.87 g kg−1 vs. 1.92 g kg−1 in LCJ S-1 and 5.19 g kg−1 vs. 0.96 g kg−1 in LCJ S-2). Soil pH ranged from 4.50 to 5.74 in the two soil profiles and generally increased with increasing soil depth. According to the percentages of clay, silt, and sand, most soil samples can be categorized as silty loam. Soil pH values were negatively correlated with C/N ratios (r = −0.66, p < 0.01) and SOC contents (r = −0.52, p < 0.01). Clay contents were positively correlated with C/N ratios (r = 0.43, p < 0.05) and SOC contents (r = 0.42, p < 0.01). The results indicate that soil pH and clay are essential factors influencing the SOC spatial distributions in the two soil profiles.

scholarly journals Geochemical Characteristics of Rare Earth Elements in Soils from Puding Karst Critical Zone Observatory, Southwest China

2019 ◽  
Vol 11 (18) ◽  
pp. 4963 ◽  
Author(s):  
Qian Zhang ◽  
Guilin Han ◽  
Man Liu ◽  
Lingqing Wang
Keyword(s):  
Rare Earth ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Rare Earth Elements ◽  
Southwest China ◽  
Clay Content ◽  
Critical Zone ◽  
Fe Content ◽  
Local Soil ◽  
Critical Zone Observatory

Soil samples from eight soil profiles under different land-use types were collected at the Puding Karst Critical Zone Observatory, Southwest China, to investigate the distribution, fractionation, and controlling factors of rare earth elements (REEs). The total REEs contents in topsoil ranged from 149.97 to 247.74 mg kg−1, the contents in most topsoil were higher than local soil background value (202.60 mg kg−1), and the highest content was observed in topsoil under cropland. The REEs contents in surface soils from lower slopes sites were higher than that of middle and upper slope sites, and the highest contents were observed in cropland. The PAAS-normalized REEs pattern in soils showed MREEs significantly enriched relative to LREEs and HREEs, and HREEs were enriched relative to LREEs. The results showed that clay content, pH, soil organic carbon, total phosphorus, and Fe content were the main factors influencing the distribution of REEs in karst soils, and soil organic carbon (SOC), Fe content showed better linear relationship with REEs.

scholarly journals Vertical Distribution and Controlling Factors Exploration of Sc, V, Co, Ni, Mo and Ba in Six Soil Profiles of The Mun River Basin, Northeast Thailand

2020 ◽  
Vol 17 (5) ◽  
pp. 1745
Author(s):  
Wenxiang Zhou ◽  
Guilin Han ◽  
Man Liu ◽  
Chao Song ◽  
Xiaoqiang Li ◽  
...  
Keyword(s):  
Heavy Metals ◽  
River Basin ◽  
Soil Ph ◽  
Soil Texture ◽  
Heavy Metal Contamination ◽  
Geoaccumulation Index ◽  
Controlling Factors ◽  
Soil Profiles ◽  
Northeast Thailand ◽  
Mo Content

Exploring the enrichment and controlling factors of heavy metals in soils is essential because heavy metals can cause severe soil contamination and threaten human health when they are excessively enriched in soils. Soil samples (total 103) from six soil profiles (T1 to T6) in the Mun River Basin, Northeast Thailand, were collected for the analyses of the content of heavy metals, including Sc, V, Co, Ni, Mo, Ba. The average contents of soil heavy metals decrease in the following order: Ba, V, Ni, Sc, Co, and Mo (T1, T3, T4 and T5); Ni, V, Ba, Co, Sc, Mo, and Ba (T2); Ba, V, Sc, Ni, Mo, and Co (T6). An enrichment factor (EF) and geoaccumulation index were calculated to assess the degree of heavy metal contamination in the soils. The EFs of these heavy metals in most samples range from 0 to 1.5, which reveals that most heavy metals are slightly enriched. Geoaccumulation indexes show that only the topsoil of T1 and T2 is slightly contaminated by Ba, Sc, Ni, and V. Soil organic carbon (SOC), soil pH and soil texture are significantly positively correlated with most heavy metals, except for a negative correlation between soil pH and Mo content. In conclusion, the influence of heavy metals on soils in the study area is slight and SOC, soil pH, soil texture dominate the behavior of heavy metals.

scholarly journals Spatial heterogeneity and environmental predictors of permafrost region soil organic carbon stocks

2021 ◽  
Vol 7 (9) ◽  
pp. eaaz5236 ◽  
Author(s):  
Umakant Mishra ◽  
Gustaf Hugelius ◽  
Eitan Shelef ◽  
Yuanhe Yang ◽  
Jens Strauss ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Heterogeneity ◽  
Surface Air Temperature ◽  
Spatially Explicit ◽  
Soil Profiles ◽  
Permafrost Region ◽  
Environmental Predictors ◽  
Soil Organic Carbon Stocks ◽  
Soc Stocks

Large stocks of soil organic carbon (SOC) have accumulated in the Northern Hemisphere permafrost region, but their current amounts and future fate remain uncertain. By analyzing dataset combining >2700 soil profiles with environmental variables in a geospatial framework, we generated spatially explicit estimates of permafrost-region SOC stocks, quantified spatial heterogeneity, and identified key environmental predictors. We estimated that 1014−175+194 Pg C are stored in the top 3 m of permafrost region soils. The greatest uncertainties occurred in circumpolar toe-slope positions and in flat areas of the Tibetan region. We found that soil wetness index and elevation are the dominant topographic controllers and surface air temperature (circumpolar region) and precipitation (Tibetan region) are significant climatic controllers of SOC stocks. Our results provide first high-resolution geospatial assessment of permafrost region SOC stocks and their relationships with environmental factors, which are crucial for modeling the response of permafrost affected soils to changing climate.

Soil organic carbon storage by shaded and unshaded coffee systems and its implications for climate change mitigation in China

2021 ◽  
pp. 1-8
Author(s):  
Ziwei Xiao ◽  
Xuehui Bai ◽  
Mingzhu Zhao ◽  
Kai Luo ◽  
Hua Zhou ◽  
...  
Keyword(s):  
Climate Change ◽  
Linear Regression ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Multiple Linear Regression ◽  
Carbon Storage ◽  
Soil Ph ◽  
Soil Bulk Density ◽  
Soil N ◽  
And Storage

Abstract Shaded coffee systems can mitigate climate change by fixation of atmospheric carbon dioxide (CO2) in soil. Understanding soil organic carbon (SOC) storage and the factors influencing SOC in coffee plantations are necessary for the development of sound land management practices to prevent land degradation and minimize SOC losses. This study was conducted in the main coffee-growing regions of Yunnan; SOC concentrations and storage of shaded and unshaded coffee systems were assessed in the top 40 cm of soil. Relationships between SOC concentration and factors affecting SOC were analysed using multiple linear regression based on the forward and backward stepwise regression method. Factors analysed were soil bulk density (ρb), soil pH, total nitrogen of soil (N), mean annual temperature (MAT), mean annual moisture (MAM), mean annual precipitation (MAP) and elevations (E). Akaike's information criterion (AIC), coefficient of determination (R2), root mean square error (RMSE) and residual sum of squares (RSS) were used to describe the accuracy of multiple linear regression models. Results showed that mean SOC concentration and storage decreased significantly with depth under unshaded coffee systems. Mean SOC concentration and storage were higher in shaded than unshaded coffee systems at 20–40 cm depth. The correlations between SOC concentration and ρb, pH and N were significant. Evidence from the multiple linear regression model showed that soil bulk density (ρb), soil pH, total nitrogen of soil (N) and climatic variables had the greatest impact on soil carbon storage in the coffee system.

Assessment of soil organic carbon stock in the upper Yangtze River basin

2013 ◽  
Vol 10 (5) ◽  
pp. 866-872 ◽  
Author(s):  
Xiao-guo Wang ◽  
Bo Zhu ◽  
Ke-ke Hua ◽  
Yong Luo ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
River Basin ◽  
Yangtze River ◽  
Carbon Stock ◽  
Yangtze River Basin ◽  
Soil Organic Carbon Stock ◽  
Upper Yangtze River ◽  
Organic Carbon Stock ◽  
The Upper Yangtze River

scholarly journals Estimating soil organic carbon stocks of Swiss forest soils by robust external-drift kriging

2014 ◽  
Vol 7 (3) ◽  
pp. 1197-1210 ◽  
Author(s):  
M. Nussbaum ◽  
A. Papritz ◽  
A. Baltensweiler ◽  
L. Walthert
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Near Infrared ◽  
Likelihood Method ◽  
Standard Errors ◽  
Soil Profiles ◽  
Near Infrared Reflectance ◽  
Soc Stock ◽  
Soc Stocks ◽  
External Drift

Abstract. Accurate estimates of soil organic carbon (SOC) stocks are required to quantify carbon sources and sinks caused by land use change at national scale. This study presents a novel robust kriging method to precisely estimate regional and national mean SOC stocks, along with truthful standard errors. We used this new approach to estimate mean forest SOC stock for Switzerland and for its five main ecoregions. Using data of 1033 forest soil profiles, we modelled stocks of two compartments (0–30, 0–100 cm depth) of mineral soils. Log-normal regression models that accounted for correlation between SOC stocks and environmental covariates and residual (spatial) auto-correlation were fitted by a newly developed robust restricted maximum likelihood method, which is insensitive to outliers in the data. Precipitation, near-infrared reflectance, topographic and aggregated information of a soil and a geotechnical map were retained in the models. Both models showed weak but significant residual autocorrelation. The predictive power of the fitted models, evaluated by comparing predictions with independent data of 175 soil profiles, was moderate (robust R2 = 0.34 for SOC stock in 0–30 cm and R2 = 0.40 in 0–100 cm). Prediction standard errors (SE), validated by comparing point prediction intervals with data, proved to be conservative. Using the fitted models, we mapped forest SOC stock by robust external-drift point kriging at high resolution across Switzerland. Predicted mean stocks in 0–30 and 0–100 cm depth were equal to 7.99 kg m−2 (SE 0.15 kg m−2) and 12.58 kg m−2 (SE 0.24 kg m−2), respectively. Hence, topsoils store about 64% of SOC stocks down to 100 cm depth. Previous studies underestimated SOC stocks of topsoil slightly and those of subsoils strongly. The comparison further revealed that our estimates have substantially smaller SE than previous estimates.

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