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ARE EXPOSURE PREDICTIONS, USED FOR THE PRIORITISATION OF PHARMACEUTICALS IN THE ENVIRONMENT, FIT FOR PURPOSE?

Overview of attention for article published in Environmental Toxicology & Chemistry, May 2017
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Mentioned by

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4 tweeters

Citations

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21 Dimensions

Readers on

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80 Mendeley
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Title
ARE EXPOSURE PREDICTIONS, USED FOR THE PRIORITISATION OF PHARMACEUTICALS IN THE ENVIRONMENT, FIT FOR PURPOSE?
Published in
Environmental Toxicology & Chemistry, May 2017
DOI 10.1002/etc.3842
Pubmed ID
Authors

Burns, Emily E., Thomas‐Oates, Jane, Kolpin, Dana W., Furlong, Edward T., Boxall, Alistair B.A., Emily E. Burns, Jane Thomas‐Oates, Dana W. Kolpin, Edward T. Furlong, Alistair B.A Boxall

Abstract

Prioritisation methodologies are often used for identifying those pharmaceuticals that pose the greatest risk to the natural environment and to focus laboratory testing or environmental monitoring towards pharmaceuticals of greatest concern. Risk-based prioritisation approaches, employing models to derive exposure concentrations, are commonly used but the reliability of these models is unclear. The present study evaluated the accuracy of exposure models commonly used for pharmaceutical prioritisation. Targeted monitoring was conducted for 95 pharmaceuticals in the Rivers Foss and Ouse in the City of York, UK. Predicted environmental concentration (PEC) ranges were estimated based on localised prescription, hydrological data, reported metabolism and wastewater treatment plant (WwTP) removal rates, and were compared to measured environmental concentrations (MECs). For the River Foss, PECs, obtained using highest metabolism and lowest WwTP removal, were similar to MECs. In contrast, this trend was not observed for the River Ouse, possibly due to pharmaceutical inputs beyond our modelling. Pharmaceuticals were ranked by risk based on either MECs or PECs. With two exceptions (dextromethorphan and diphenhydramine), risk ranking based on both MECs and PECs produced similar results in the River Foss. Overall, these findings indicate that PECs may well be appropriate for prioritisation of pharmaceuticals in the environment when robust and local data on the system of interest are available and reflective of most source inputs to the system. This article is protected by copyright. All rights reserved.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 80 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 34%
Researcher 13 16%
Student > Bachelor 11 14%
Student > Master 9 11%
Student > Doctoral Student 5 6%
Other 7 9%
Unknown 8 10%
Readers by discipline Count As %
Environmental Science 33 41%
Chemistry 10 13%
Agricultural and Biological Sciences 7 9%
Biochemistry, Genetics and Molecular Biology 5 6%
Pharmacology, Toxicology and Pharmaceutical Science 3 4%
Other 11 14%
Unknown 11 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 25 September 2017.
All research outputs
#9,079,093
of 15,822,401 outputs
Outputs from Environmental Toxicology & Chemistry
#2,881
of 4,585 outputs
Outputs of similar age
#129,029
of 267,748 outputs
Outputs of similar age from Environmental Toxicology & Chemistry
#24
of 73 outputs
Altmetric has tracked 15,822,401 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,585 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 34th percentile – i.e., 34% 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 267,748 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 50% of its contemporaries.
We're also able to compare this research output to 73 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 58% of its contemporaries.