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Modeling groundwater nitrate concentrations in private wells in Iowa

Overview of attention for article published in Science of the Total Environment, July 2015
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Title
Modeling groundwater nitrate concentrations in private wells in Iowa
Published in
Science of the Total Environment, July 2015
DOI 10.1016/j.scitotenv.2015.07.080
Pubmed ID
Authors

David C. Wheeler, Bernard T. Nolan, Abigail R. Flory, Curt T. DellaValle, Mary H. Ward

Abstract

Contamination of drinking water by nitrate is a growing problem in many agricultural areas of the country. Ingested nitrate can lead to the endogenous formation of N-nitroso compounds, potent carcinogens. We developed a predictive model for nitrate concentrations in private wells in Iowa. Using 34,084 measurements of nitrate in private wells, we trained and tested random forest models to predict log nitrate levels by systematically assessing the predictive performance of 179 variables in 36 thematic groups (well depth, distance to sinkholes, location, land use, soil characteristics, nitrogen inputs, meteorology, and other factors). The final model contained 66 variables in 17 groups. Some of the most important variables were well depth, slope length within 1km of the well, year of sample, and distance to nearest animal feeding operation. The correlation between observed and estimated nitrate concentrations was excellent in the training set (r-square=0.77) and was acceptable in the testing set (r-square=0.38). The random forest model had substantially better predictive performance than a traditional linear regression model or a regression tree. Our model will be used to investigate the association between nitrate levels in drinking water and cancer risk in the Iowa participants of the Agricultural Health Study cohort.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 <1%
Colombia 1 <1%
Unknown 114 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 24%
Student > Master 18 16%
Researcher 15 13%
Student > Doctoral Student 8 7%
Professor 5 4%
Other 12 10%
Unknown 30 26%
Readers by discipline Count As %
Environmental Science 26 22%
Engineering 14 12%
Earth and Planetary Sciences 13 11%
Agricultural and Biological Sciences 9 8%
Medicine and Dentistry 3 3%
Other 13 11%
Unknown 38 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 July 2015.
All research outputs
#22,758,309
of 25,373,627 outputs
Outputs from Science of the Total Environment
#25,926
of 29,625 outputs
Outputs of similar age
#235,024
of 274,988 outputs
Outputs of similar age from Science of the Total Environment
#189
of 210 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 29,625 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 274,988 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 210 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.