Evidence map›Paper›PMID 28527143›Full record

ArticleEnvironmental science and pollution research international2017

Characterization of arsenic availability in dry and flooded soils using sequential extraction and diffusive gradients in thin films (DGT) techniques.

Liping Zhang, Qin Sun, Shiming Ding, Xiang Cheng, Qin Liu, Chaosheng Zhang

Abstract read
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Article in Environmental science and pollution research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Liping ZhangKey Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing, 210098, China.
Qin SunKey Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing, 210098, China. sunqinnj@hhu.edu.cn.
Shiming DingState Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China. smding@niglas.ac.cn.
Xiang ChengKey Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing, 210098, China.
Qin LiuKey Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing, 210098, China.
Chaosheng ZhangInternational Network for Environment and Health, National University of Ireland, Galway, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Much attention has been paid to the availability of arsenic (As) in soils, while few studies were carried out on the comparison between the dry and flooded soils using different methods. In this study, chemical sequential extraction and diffusive gradients in thin films (DGT) techniques were employed to study the availability of As in soils amended with a range of exogenous As followed by one-year incubation under dry and flooded conditions, respectively. The results showed that the proportions of available solid As pools, including non-specifically adsorbed As (F1) and specifically adsorbed As (F2), had consistent increases with the increase of As amendment. The concentration of DGT-measured As (C

Indexed as

FloodsArsenicDiffusionSoilSoil PollutantsArsenicSoilSoil PollutantsArsenicAvailabilityDGT-induced fluxes in soilsDiffusive gradients in thin filmsSequential extraction

Identifiers

PMID28527143

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.