Evidence map›Paper›PMID 32535281›Full record

ArticleThe Science of the total environment2020

Reduction in drinking water arsenic exposure and health risk through arsenic treatment among private well households in Maine and New Jersey, USA.

Qiang Yang, Sara V Flanagan, Steven Chillrud, James Ross, Wenke Zeng, Charles Culbertson, Steven Spayd, Lorraine Backer, Andrew E Smith, Yan Zheng

Open access · greenAbstract read
In one paragraph

Article in The Science of the total environment, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.6field-weighted citation impact, top 19% of its field
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

9 citing papers in PubMed, 31 citations in OpenAlex.

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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

10 authors at 4 institutions in 2 countries.

Qiang YangLamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA.
Sara V FlanaganLamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA.
Steven ChillrudLamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA.
James RossLamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA.
Wenke ZengSchool of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China; Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA.
Charles CulbertsonU.S. Geological Survey, New England Water Science Center, Augusta, ME 04330, USA.
Steven SpaydNew Jersey Geological and Water Survey, Trenton, NJ 08625, USA.
Lorraine BackerNational Center for Environmental Health, U.S. Centers for Disease Control and Prevention, Atlanta, GA 30341, USA.
Andrew E SmithMaine Department of Health and Human Services, Center for Disease Control and Prevention, Augusta, ME 04333, USA.
Yan ZhengSchool of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China; Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA. Electronic address: yan.zheng@sustech.edu.cn.
Lamont-Doherty Earth Observatory · USCenters for Disease Control and Prevention · USState of Maine Department of Health and Human Services · USUnited States Geological Survey · US

Funding

Training CoreP42ES010349 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI NAVAS-ACIEN, ANA · 2000 to 2020
$53.6M
True Metal fac coreP30ES009089 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI MATTHEW S PERZANOWSKI · 1998 to 2026
$44.7M
NCEH CDC HHS UE2 EH001326NIEHS NIH HHS P30 ES009089NIEHS NIH HHS P42 ES010349
6 · The paper itself

Abstract

Over 2 million mostly rural Americans are at risk of drinking water from private wells that contain arsenic (As) exceeding the U.S. Environmental Protection Agency (USEPA) Maximum Contaminant Level (MCL) of 10 micrograms per liter (μg/L). How well existing treatment technologies perform in real world situations, and to what extent they reduce health risks, are not well understood. This study evaluates the effectiveness of household As treatment systems in southern-central Maine (ME, n = 156) and northern New Jersey (NJ, n = 94) and ascertains how untreated well water chemistry and other factors influence As removal. Untreated and treated water samples, as well as a treatment questionnaire, were collected. Most ME households had point-of-use reverse-osmosis systems (POU RO), while in NJ, dual-tank point-of-entry (POE) whole house systems were popular. Arsenic treatment systems reduced well water arsenic concentrations ([As]) by up to two orders of magnitude, i.e. from a median of 71.7 to 0.8 μg/L and from a mean of 105 to 14.3 μg/L in ME, and from a median of 8.6 to 0.2 μg/L and a mean of 15.8 to 2.1 μg/L in NJ. More than half (53%) of the systems in ME reduced water [As] to below 1 μg/L, compared to 69% in NJ. The treatment system failure rates were 19% in ME (>USEPA MCL of 10 μg/L) and 16% in NJ (>NJ MCL of 5 μg/L). In both states, the higher the untreated well water [As] and the As(III)/As ratio, the higher the rate of treatment failure. POE systems failed less than POU systems, as did the treatment systems installed and maintained by vendors than those by homeowners. The 7-fold reduction of [As] in the treated water reduced skin cancer risk alone from 3765 to 514 in 1 million in ME, and from 568 to 75 in 1 million in NJ.

Indexed as

Drinking WaterArsenicEnvironmental MonitoringMaineNew JerseyUnited StatesWater Pollutants, ChemicalWater SupplyWater WellsArsenicDrinking WaterWater Pollutants, ChemicalArsenic removalExposureHealth riskPoint-of-entryPoint-of-usePrivate well

Identifiers

PMID32535281
PMCPMC7429269
OpenAlexW3028566140

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.