Arsenic in Soils: Natural Concentration and Adsorption by Oxisols Developed From Different Lithologies
Bibliographic record
Abstract
Arsenic (As) is a toxic and a carcinogenic element naturally occurring in the environment. Therefore, studies on As natural concentration in soils and its adsorption process are important tools for the evaluation of potential risks of soil contamination in order to adoption of control actions or monitoring of potential As-contamination sources. The objective of this study was to evaluate the natural levels of As and determine the maximum adsorption capacity of As (MACAs) of six Oxisols (Latossolos) of Minas Gerais State, Brazil, developed from different lithologies. The soil sample’s total As content was determined using the USEPA 3051A method. The adsorption experiments were performed using different As concentrations, and the MACAs was measured by the Langmuir isotherm. On average, the natural As content in Oxisols was 13.13 mg kg-1, which is above the reference value of soil quality (RVQ) for As, in Brazil (8 mg kg-1). The levels of As in Oxisols originated from metamorphic/igneous rocks were significantly higher than those of Oxisols from sedimentary rocks. Globally, the evaluated soils showed a mean MACAs equal to 2,548 mg kg-1. Soil horizon Bw showed a higher MACAs than that of A horizon. In general, the levels of clay, iron oxides, iron forms (especially poorly-crystallized) and organic carbon had a positive influence on MACAs. Although the RVQ for As is well below the MACAs in all soils, the soil adsorbed As naturally present, rendering it unavailable in the soil aqueous phase. Therefore, there was no risk of contamination for human health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".