Dataset Information

Modeling and Assessing the Spatial and Vertical Distributions of Potentially Toxic Elements in Soil and How the Concentrations Differ.

ABSTRACT: A healthy soil is a healthy ecosystem because humans, animals, plants, and water highly depend upon it. Soil pollution by potentially toxic elements (PTEs) is a serious concern for humankind. The study is aimed at (i) assessing the concentrations of PTEs in soils under a long-term heavily industrialized region for coal and textiles, (ii) modeling and mapping the spatial and vertical distributions of PTEs using a GIS-based ordinary kriging technique, and (iii) identifying the possible sources of these PTEs in the Jizerské Mountains (Jizera Mts.) using a positive matrix factorization (PMF) model. Four hundred and forty-two (442) soil samples were analyzed by applying the aqua regia method. To assess the PTE contents, the level of pollution, and the distribution pattern in soil, the contamination factor (CF) and the pollution load index load (PLI) were applied. ArcGIS-based ordinary kriging interpolation was used for the spatial analysis of PTEs. The results of the analysis revealed that the variation in the coefficient (CV) of PTEs in the organic soil was highest in Cr (96.36%), followed by Cu (54.94%) and Pb (49.40%). On the other hand, the mineral soil had Cu (96.88%), Cr (66.70%), and Pb (64.48%) as the highest in CV. The PTEs in both the organic soil and the mineral soil revealed a high heterogeneous variability. Though the study area lies within the "Black Triangle", which is a historic industrial site in Central Europe, this result did not show a substantial influence of the contamination of PTEs in the area. In spite of the rate of pollution in this area being very low based on the findings, there may be a need for intermittent assessment of the soil. This helps to curtail any excessive accumulation and escalation in future. The results may serve as baseline information for pollution assessment. It might support policy-developers in sustainable farming and forestry for the health of an ecosystem towards food security, forest safety, as well as animal and human welfare.

SUBMITTER: Ahado SK

PROVIDER: S-EPMC8402386 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Soil microbial biomass carbon (SMBC) is important in regulating soil organic carbon (SOC) dynamics along soil profiles by mediating the decomposition and formation of SOC. The dataset (VDMBC) is about the vertical distributions of SOC, SMBC, and soil microbial quotient (SMQ?=?SMBC/SOC) and their relations to environmental factors across five continents. Data were collected from literature, with a total of 289 soil profiles and 1040 observations in different soil layers compiled. The associated environment data collectd include climate, ecosystem types, and edaphic factors. We developed this dataset by searching the Web of Sciene and the China National Knowledge Infrastructure from the year of 1970 to 2019. All the data in this dataset met two creteria: 1) there were at least three mineral soil layers along a soil profile, and 2) SMBC was measured using the fumigation extraction method. The data in tables and texts were obtained from literature directly, and the data in figures were extracted by using the GetData Graph digitizer software version 2.25. When climate and soil properties were not available from publications, we obtainted the data from the World Weather Information Service (https://worldweather.wmo.int/en/home.html) and SoilGrids at a spatial resolution of 250 meters (version 0.5.3, https://soilgrids.org). The units of all the variables were converted to the standard international units or commonly used ones and the values were transformed correspondingly. For example, the value of soil organic matter (SOM) was converted to SOC by using the equation (SOC?=?SOM?×?0.58). This dataset can be used in predicting global SOC changes along soil profiles by using the multi-layer soil carbon models. It can also be used to analyse how soil microbial biomass changes with plant roots as well as the composition, structure, and functions of soil microbial communities along soil profiles at large spatial scales. This dataset offers opportunities to improve our prediction of SOC dynamics under global changes and to advance our understanding of the environmental controls.

Dataset Information

Modeling and Assessing the Spatial and Vertical Distributions of Potentially Toxic Elements in Soil and How the Concentrations Differ.

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