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High-Speed Limnology: Using Advanced Sensors to Investigate Spatial Variability in Biogeochemistry and Hydrology

Overview of attention for article published in Environmental Science & Technology, December 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 (95th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
27 tweeters
facebook
2 Facebook pages
googleplus
1 Google+ user

Citations

dimensions_citation
35 Dimensions

Readers on

mendeley
116 Mendeley
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Title
High-Speed Limnology: Using Advanced Sensors to Investigate Spatial Variability in Biogeochemistry and Hydrology
Published in
Environmental Science & Technology, December 2014
DOI 10.1021/es504773x
Pubmed ID
Authors

John T. Crawford, Luke C. Loken, Nora J. Casson, Colin Smith, Amanda G. Stone, Luke A. Winslow

Abstract

Advanced sensor technology is widely used in aquatic monitoring and research. Most applications focus on temporal variability, whereas spatial variability has been challenging to document. We assess the capability of water chemistry sensors embedded in a high-speed water intake system to document spatial variability. This new sensor platform continuously samples surface water at a range of speeds (0 to > 45 km hr(-1)) resulting in high-density, meso-scale spatial data. These novel observations reveal previously unknown variability in physical, chemical, and biological factors in streams, rivers, and lakes. By combining multiple sensors into one platform, we were able to detect terrestrial-aquatic hydrologic connections in a small dystrophic lake, to infer the role of main-channel vs. backwater nutrient processing in a large river, and to detect sharp chemical changes across aquatic ecosystem boundaries in a stream/lake complex. Spatial sensor data were verified in our examples by comparing with standard lab-based measurements of selected variables. Spatial fDOM data showed strong correlation with wet chemistry measurements of DOC, and optical NO3 concentrations were highly correlated with lab-based measurements. High-frequency spatial data similar to our examples could be used to further understand aquatic biogeochemical fluxes, ecological patterns, and ecosystem processes, and will both inform and benefit from fixed-site data.

Twitter Demographics

The data shown below were collected from the profiles of 27 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 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 6%
Canada 2 2%
Japan 1 <1%
Unknown 106 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 28%
Student > Master 22 19%
Student > Ph. D. Student 20 17%
Professor > Associate Professor 8 7%
Student > Bachelor 7 6%
Other 18 16%
Unknown 9 8%
Readers by discipline Count As %
Environmental Science 46 40%
Agricultural and Biological Sciences 22 19%
Earth and Planetary Sciences 14 12%
Engineering 9 8%
Chemistry 3 3%
Other 6 5%
Unknown 16 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 33. 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 05 November 2015.
All research outputs
#364,585
of 11,337,069 outputs
Outputs from Environmental Science & Technology
#643
of 11,618 outputs
Outputs of similar age
#10,531
of 252,108 outputs
Outputs of similar age from Environmental Science & Technology
#26
of 240 outputs
Altmetric has tracked 11,337,069 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,618 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. This one has done particularly well, scoring higher than 94% 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 252,108 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 95% of its contemporaries.
We're also able to compare this research output to 240 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.