Evidence map›Paper›PMID 34378124›Full record

ArticleEnvironmental science and pollution research international2022

Linking river sediment arsenic to catchment spatial attributes in agricultural landscapes.

Hamidreza Shahradnia, Atefeh Chamani, Mehrdad Zamanpoore

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Article in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Hamidreza ShahradniaEnvironmental Science Department, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.
Atefeh ChamaniEnvironmental Science Department, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran. atefehchamani@yahoo.com.ORCID http://orcid.org/0000-0002-9463-8579
Mehrdad ZamanpooreDepartment of Hydrobiology, Agricultural Research Education and Extension Organization, Fars Agricultural and Natural Resources Research and Education Center, Shiraz, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding of the sources and processes involved in the heavy metal accumulation in river sediments is important for measuring the risks associated with human exposure. Hence, an integrated modeling approach was designed to study the linkage between landscape-related natural and anthropogenic features and high arsenic levels at the outlet of six catchments on the Ghareh-Ajagh River, central Iran. Sediment arsenic levels were measured in 8 months from October 2018 to November 2019 when the river sediment and water conditions were stable and ranged from 16.3 to 78.5 mg/kg. Monthly catchment-level agricultural areas were extracted from Landsat 8-OLI images. Predictive variables included NDVI values; area and spatial patterns of agriculture measured using four landscape metrics of NP, PD, MPS, and ENN; length and slope of the streams extended from main agricultural areas to the catchment outlet; and four hydrologic soil groups. The best-fitted multiple regression model (r

Indexed as

ArsenicRiversAgricultureEnvironmental MonitoringGeologic SedimentsHumansSoilArsenicSoilAgricultureArsenicLandscape metricsMultiple linear regression

Identifiers

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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.