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Predicting Arsenic in Drinking Water Wells of the Central Valley, California

Overview of attention for article published in Environmental Science & Technology, July 2016
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  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

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7 tweeters

Citations

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46 Dimensions

Readers on

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86 Mendeley
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Title
Predicting Arsenic in Drinking Water Wells of the Central Valley, California
Published in
Environmental Science & Technology, July 2016
DOI 10.1021/acs.est.6b01914
Pubmed ID
Authors

Joseph D. Ayotte, Bernard T. Nolan, Jo Ann Gronberg

Abstract

Probabilities of arsenic in groundwater at depths used for domestic and public supply in the Central Valley of California are predicted using weak-learner ensemble models (boosted regression trees, BRT) and more traditional linear models (logistic regression, LR). Both methods captured major processes that affect arsenic concentrations, such as the chemical evolution of groundwater, redox differences, and the influence of aquifer geochemistry. Inferred flow-path length was the most important variable but near-surface-aquifer geochemical data also were significant. A unique feature of this study was that previously predicted nitrate concentrations in three dimensions were themselves predictive of arsenic and indicated an important redox effect at >10 μg/L, indicating low arsenic where nitrate was high. Additionally, a variable representing three-dimensional aquifer texture from the Central Valley Hydrologic Model was an important predictor, indicating high arsenic associated with fine-grained aquifer sediment. BRT outperformed LR at the 5 μg/L threshold in all five predictive performance measures and at 10 μg/L in four out of five measures. BRT yielded higher prediction sensitivity (39%) than LR (18%) at the 10 μg/L threshold-a useful outcome because a major objective of the modeling was to improve our ability to predict high arsenic areas.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 86 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 30%
Researcher 16 19%
Student > Bachelor 7 8%
Student > Master 6 7%
Student > Doctoral Student 5 6%
Other 14 16%
Unknown 12 14%
Readers by discipline Count As %
Environmental Science 24 28%
Earth and Planetary Sciences 18 21%
Engineering 8 9%
Chemistry 6 7%
Materials Science 2 2%
Other 7 8%
Unknown 21 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 21 May 2019.
All research outputs
#3,942,187
of 15,418,109 outputs
Outputs from Environmental Science & Technology
#5,156
of 14,908 outputs
Outputs of similar age
#70,323
of 261,494 outputs
Outputs of similar age from Environmental Science & Technology
#98
of 248 outputs
Altmetric has tracked 15,418,109 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 14,908 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.6. This one has gotten more attention than average, scoring higher than 65% 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 261,494 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 248 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.