Evidence map›Paper›PMID 32192969›Full record

ArticleThe Science of the total environment2020

Patterns and predictions of drinking water nitrate violations across the conterminous United States.

Michael J Pennino, Scott G Leibowitz, Jana E Compton, Ryan A Hill, Robert D Sabo

Abstract 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 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Review
  2. Article
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  6. Article
  7. Prioritizing river basins for nutrient studies.Environmental monitoring and assessment · 2024
    Article
  8. Article
  9. Nutrient Explorer: An analytical framework to visualize and investigate drivers of surface water quality.Environmental modelling & software : with environment data news · 2023
    Article
  10. Article
  11. Article
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  15. Article
  16. Article
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  18. Article
  19. Coupling the dual isotopes of water (δEnvironmental research letters : ERL [Web site] · 2021
    Article
  20. 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.

Michael J PenninoU.S. EPA, Office of Research and Development, Center for Public Health and Environmental Assessment, Health & Environmental Effects Assessment Division, Washington, DC, USA. Electronic address: pennino.michael@epa.gov.
Scott G LeibowitzU.S. EPA, Office of Research and Development, Center for Public Health and Environmental Assessment, Pacific Ecological Systems Division, Corvallis, OR, USA.
Jana E ComptonU.S. EPA, Office of Research and Development, Center for Public Health and Environmental Assessment, Pacific Ecological Systems Division, Corvallis, OR, USA.
Ryan A HillU.S. EPA, Office of Research and Development, Center for Public Health and Environmental Assessment, Pacific Ecological Systems Division, Corvallis, OR, USA.
Robert D SaboU.S. EPA, Office of Research and Development, Center for Public Health and Environmental Assessment, Health & Environmental Effects Assessment Division, Washington, DC, USA.

Funding

Intramural EPA EPA999999
6 · The paper itself

Abstract

Excess nitrate in drinking water is a human health concern, especially for young children. Public drinking water systems in violation of the 10 mg nitrate-N/L maximum contaminant level (MCL) must be reported in EPA's Safe Drinking Water Information System (SDWIS). We used SDWIS data with random forest modeling to examine the drivers of nitrate violations across the conterminous U.S. and to predict where public water systems are at risk of exceeding the nitrate MCL. As explanatory variables, we used land cover, nitrogen inputs, soil/hydrogeology, and climate variables. While we looked at the role of nitrate treatment in separate analyses, we did not include treatment as a factor in the final models, due to incomplete information in SDWIS. For groundwater (GW) systems, a classification model correctly classified 79% of catchments in violation and a regression model explained 43% of the variation in nitrate concentrations above the MCL. The most important variables in the GW classification model were % cropland, agricultural drainage, irrigation-to-precipitation ratio, nitrogen surplus, and surplus precipitation. Regions predicted to have risk for nitrate violations in GW were the Central California Valley, parts of Washington, Idaho, the Great Plains, Piedmont of Pennsylvania and Coastal Plains of Delaware, and regions of Wisconsin, Iowa, and Minnesota. For surface water (SW) systems, a classification model correctly classified 90% of catchments and a regression model explained 52% of the variation in nitrate concentration. The variables most important for the SW classification model were largely hydroclimatic variables including surplus precipitation, irrigation-to-precipitation ratio, and % shrubland. Areas at greatest risk for SW nitrate violations were generally in the non-mountainous west and southwest. Identifying the areas with possible risk for future violations and potential drivers of nitrate violations across U.S. can inform decisions on how source water protection and other management options could best protect drinking water.

Indexed as

Drinking WaterNitratesUnited StatesWater Pollutants, ChemicalWater SupplyDrinking WaterNitratesWater Pollutants, ChemicalDrinking waterGroundwaterNitrateRandom forest modelingRiskSurface water

Identifiers

PMID32192969
PMCPMC8204728

What OpenQuestion holds

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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.