Stratum Affects the Distribution of Soil Selenium Bioavailability by Modulating the Soil Physicochemical Properties: A Case Study in a Se-enriched Area, China
JOURNAL OF ENVIRONMENTAL MANAGEMENT(2024)
Chongqing Normal Univ
Abstract
The soil selenium (Se) content and bioavailability are important for human health. In this regard, knowing the factors driving the concentration of total Se and bioavailable Se in soils is essential to map Se, enhance foodstuffs' Se content, and improve the Se nutritional status of humans. In this study, total Se and Se bioavailability (i.e., phosphate extracted Se) in surface soils (0-20 cm) developed on different strata were analyzed in a Se-enriched region of Southwest China. Furthermore, the interaction between the stratum and soil properties was assessed and how did the stratum effect on the concentration and spatial distribution of Se bioavailability in soils was investigated. Results showed that the median concentration of total Se in soils was 0.308 mg/kg, which is higher than China's soil background. The mean proportion of phosphate extracted Se in total Se was 12.2 %. The values of total Se, phosphate extracted Se, and soil organic matter (SOM) in soils increased with the increasing stratum age. In contrast, the coefficient of weathering and eluviation (BA) values decreased. The analysis of statistics and Geodetector revealed that the SOM, stratum, and BA were the dominant controlling factors for the contents and distributions of soil total Se and phosphate extracted Se. This study provided strong evidence that the soil properties that affected the total Se and Se bioavailability were modulated by the local geological background, and had important practical implications for addressing Se malnutrition and developing the Se-rich resource in the study region and similar geological settings in different parts of the globe.
MoreTranslated text
Key words
Selenium,Stratum,Soil organic matter,Selenium bioavailability
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined