Evidence map›Paper›PMID 29670010›Full record

ArticleInternational journal of environmental research and public health2018

Evaluating Geologic Sources of Arsenic in Well Water in Virginia (USA).

Tiffany VanDerwerker, Lin Zhang, Erin Ling, Brian Benham, Madeline Schreiber

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2018. 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

5 authors.

Tiffany VanDerwerkerDepartment of Geosciences, Virginia Tech, Blacksburg, VA 24061, USA. tiffanyvanderwerker@gmail.com.
Lin ZhangDepartment of Statistics, Virginia Tech, Blacksburg, VA 24061, USA. linzhang@vt.edu.
Erin LingDepartment of Biological Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA. ejames@vt.edu.
Brian BenhamDepartment of Biological Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA. benham@vt.edu.
Madeline SchreiberDepartment of Geosciences, Virginia Tech, Blacksburg, VA 24061, USA. mschreib@vt.edu.ORCID 0000-0002-1858-7730

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We investigated if geologic factors are linked to elevated arsenic (As) concentrations above 5 &mu;g/L in well water in the state of Virginia, USA. Using geologic unit data mapped within GIS and two datasets of measured As concentrations in well water (one from public wells, the other from private wells), we evaluated occurrences of elevated As (above 5 &mu;g/L) based on geologic unit. We also constructed a logistic regression model to examine statistical relationships between elevated As and geologic units. Two geologic units, including Triassic-aged sedimentary rocks and Triassic-Jurassic intrusives of the Culpeper Basin in north-central Virginia, had higher occurrences of elevated As in well water than other geologic units in Virginia. Model results support these patterns, showing a higher probability for As occurrence above 5 &mu;g/L in well water in these two units. Due to the lack of observations (<5%) having elevated As concentrations in our data set, our model cannot be used to predict As concentrations in other parts of the state. However, our results are useful for identifying areas of Virginia, defined by underlying geology, that are more likely to have elevated As concentrations in well water. Due to the ease of obtaining publicly available data and the accessibility of GIS, this study approach can be applied to other areas with existing datasets of As concentrations in well water and accessible data on geology.

Indexed as

Water WellsArsenicEnvironmental MonitoringGeological PhenomenaVirginiaWater Pollutants, ChemicalWater SupplyArsenicWater Pollutants, Chemicaldrinking watergroundwater managementlogistic regressionstatistical modelingwater quality

Identifiers

PMID29670010
PMCPMC5923829

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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.