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Mississippi River Nitrate Loads from High Frequency Sensor Measurements and Regression-Based Load Estimation

Overview of attention for article published in Environmental Science & Technology, October 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
7 news outlets
blogs
1 blog
twitter
10 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
76 Dimensions

Readers on

mendeley
108 Mendeley
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Title
Mississippi River Nitrate Loads from High Frequency Sensor Measurements and Regression-Based Load Estimation
Published in
Environmental Science & Technology, October 2014
DOI 10.1021/es504029c
Pubmed ID
Authors

Brian A. Pellerin, Brian A. Bergamaschi, Robert J. Gilliom, Charles G. Crawford, JohnFranco Saraceno, C. Paul Frederick, Bryan D. Downing, Jennifer C. Murphy

Abstract

Accurately quantifying nitrate (NO3(-)) loading from the Mississippi River is important for predicting summer hypoxia in the Gulf of Mexico and targeting nutrient reduction within the basin. Loads have historically been modeled with regression-based techniques, but recent advances with high frequency NO3(-) sensors allowed us to evaluate model performance relative to measured loads in the lower Mississippi River. Patterns in NO3(-) concentrations and loads were observed at daily to annual time steps, with considerable variability in concentration-discharge relationships over the two year study. Differences were particularly accentuated during the 2012 drought and 2013 flood, which resulted in anomalously high NO3- concentrations consistent with a large flush of stored NO3(-) from soil. The comparison between measured loads and modeled loads (LOADEST, Composite Method, WRTDS) showed underestimates of only 3.5% across the entire study period, but much larger differences at shorter time steps. Absolute differences in loads were typically greatest in the spring and early summer critical to Gulf hypoxia formation, with the largest differences (underestimates) for all models during the flood period of 2013. In additional to improving the accuracy and precision of monthly loads, high frequency NO3(-)measurements offer additional benefits not available with regression-based or other load estimation techniques.

Twitter Demographics

The data shown below were collected from the profiles of 10 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 108 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 6%
Japan 1 <1%
Unknown 101 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 26%
Student > Master 21 19%
Student > Ph. D. Student 17 16%
Student > Bachelor 9 8%
Professor > Associate Professor 6 6%
Other 11 10%
Unknown 16 15%
Readers by discipline Count As %
Environmental Science 39 36%
Earth and Planetary Sciences 17 16%
Engineering 15 14%
Agricultural and Biological Sciences 9 8%
Social Sciences 2 2%
Other 5 5%
Unknown 21 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 69. 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 08 June 2016.
All research outputs
#205,663
of 12,313,065 outputs
Outputs from Environmental Science & Technology
#348
of 12,334 outputs
Outputs of similar age
#4,541
of 223,189 outputs
Outputs of similar age from Environmental Science & Technology
#8
of 275 outputs
Altmetric has tracked 12,313,065 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,334 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.1. This one has done particularly well, scoring higher than 97% of its peers.
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 223,189 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 275 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.