Evidence map›Paper›PMID 34218089›Full record

ArticleWater research2021

Assessing cyanobacterial frequency and abundance at surface waters near drinking water intakes across the United States.

Megan M Coffer, Blake A Schaeffer, Katherine Foreman, Alex Porteous, Keith A Loftin, Richard P Stumpf, P Jeremy Werdell, Erin Urquhart, Ryan J Albert, John A Darling

Abstract read
In one paragraph

Article in Water research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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  5. CyanoHABs and CAPs: assessing community-based monitoring of PMEnvironmental science: atmospheres · 2025
    Article
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  7. Article
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  12. Review
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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.

Megan M CofferORISE Fellow, U.S. Environmental Protection Agency, Office of Research and Development, Durham, NC, USA; Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA. Electronic address: coffer.megan@epa.gov.
Blake A SchaefferU.S. Environmental Protection Agency, Office of Research and Development, Durham, NC, USA.
Katherine ForemanU.S. Environmental Protection Agency, Office of Water, Washington, DC, USA.
Alex PorteousU.S. Environmental Protection Agency, Office of Water, Washington, DC, USA.
Keith A LoftinU.S. Geological Survey, Kansas Water Science Center, Lawrence, KS, USA.
Richard P StumpfNational Oceanic and Atmospheric Administration, National Centers for Coastal Ocean Science, Silver Spring, MD, USA.
P Jeremy WerdellOcean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA.
Erin UrquhartScience Systems and Applications, Inc., Ocean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA.
Ryan J AlbertU.S. Environmental Protection Agency, Office of Water, Washington, DC, USA.
John A DarlingU.S. Environmental Protection Agency, Office of Research and Development, Durham, NC, USA.

Funding

Intramural EPA EPA999999
6 · The paper itself

Abstract

This study presents the first large-scale assessment of cyanobacterial frequency and abundance of surface water near drinking water intakes across the United States. Public water systems serve drinking water to nearly 90% of the United States population. Cyanobacteria and their toxins may degrade the quality of finished drinking water and can lead to negative health consequences. Satellite imagery can serve as a cost-effective and consistent monitoring technique for surface cyanobacterial blooms in source waters and can provide drinking water treatment operators information for managing their systems. This study uses satellite imagery from the European Space Agency's Ocean and Land Colour Instrument (OLCI) spanning June 2016 through April 2020. At 300-m spatial resolution, OLCI imagery can be used to monitor cyanobacteria in 685 drinking water sources across 285 lakes in 44 states, referred to here as resolvable drinking water sources. First, a subset of satellite data was compared to a subset of responses (n = 84) submitted as part of the U.S. Environmental Protection Agency's fourth Unregulated Contaminant Monitoring Rule (UCMR 4). These UCMR 4 qualitative responses included visual observations of algal bloom presence and absence near drinking water intakes from March 2018 through November 2019. Overall agreement between satellite imagery and UCMR 4 qualitative responses was 94% with a Kappa coefficient of 0.70. Next, temporal frequency of cyanobacterial blooms at all resolvable drinking water sources was assessed. In 2019, bloom frequency averaged 2% and peaked at 100%, where 100% indicated a bloom was always present at the source waters when satellite imagery was available. Monthly cyanobacterial abundances were used to assess short-term trends across all resolvable drinking water sources and effect size was computed to provide insight on the number of years of data that must be obtained to increase confidence in an observed change. Generally, 2016 through 2020 was an insufficient time period for confidently observing changes at these source waters; on average, a decade of satellite imagery would be required for observed environmental trends to outweigh variability in the data. However, five source waters did demonstrate a sustained short-term trend, with one increasing in cyanobacterial abundance from June 2016 to April 2020 and four decreasing.

Indexed as

CyanobacteriaDrinking WaterEnvironmental MonitoringEutrophicationLakesUnited StatesDrinking WaterCyanobacteriaDrinking waterInland waterRemote sensingSource water qualityWater quality

Identifiers

PMID34218089
PMCPMC8908444

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.