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An improved method for calculating toxicity‐based pollutant loads: Part 2. Application to contaminants discharged to the Great Barrier Reef, Queensland, Australia

Overview of attention for article published in Integrated Environmental Assessment & Management, December 2016
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Title
An improved method for calculating toxicity‐based pollutant loads: Part 2. Application to contaminants discharged to the Great Barrier Reef, Queensland, Australia
Published in
Integrated Environmental Assessment & Management, December 2016
DOI 10.1002/ieam.1860
Pubmed ID
Authors

Rachael A Smith, Michael St J Warne, Kerrie Mengersen, Ryan DR Turner

Abstract

Pollutant loads are widely used to set pollution reduction targets and assess regulatory compliance for the protection of receiving waterbodies. However, when a pollutant load is comprised of a mixture of chemicals, reducing the overall load (mass) will not necessarily reduce the toxicity by a similar amount. This can be overcome by setting targets based on toxicity-based loads (toxic loads), where the load is modified according to the relative toxicity (expressed as toxic equivalency factors- TEFs) of each toxicant. Here we present the second paper of a two-part series in which a case study is used to demonstrate the application of the toxic load method proposed in Part 1. The toxic load method converts a pollutant load, comprised of multiple chemicals, to a toxicity-based load (toxic load), using a modified TEF approach. The modified approach is based upon the cumulative distribution of relative potency (ReP) estimates of multiple species, and is further improvedon previously published TEF methods with the inclusion of two tests to select the percentile of the cumulative ReP distribution which generate TLs: that align with an independent mixture method (test for environmental relevance); and are independent of the reference chemical used (test for robustness). Here, the TL method is applied to mixtures of pesticides that are discharged from agricultural land to the Great Barrier Reef (GBR) in order to test its utility. In this case study, the most environmentally relevant and robust TLs were generated using the 75(th) percentile of the ReP cumulative distribution. The results demonstrate that it is essential to develop pollution reduction targets based on toxic loads, and making progress to meeting them will lead to a commensurate reduction in toxic effects caused by toxicants in waters of the GBR. This article is protected by copyright. All rights reserved.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 33%
Other 3 9%
Student > Ph. D. Student 3 9%
Student > Master 2 6%
Librarian 2 6%
Other 3 9%
Unknown 9 27%
Readers by discipline Count As %
Environmental Science 15 45%
Agricultural and Biological Sciences 3 9%
Engineering 2 6%
Business, Management and Accounting 1 3%
Chemistry 1 3%
Other 1 3%
Unknown 10 30%
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 30 October 2016.
All research outputs
#19,962,154
of 25,394,764 outputs
Outputs from Integrated Environmental Assessment & Management
#610
of 975 outputs
Outputs of similar age
#298,460
of 416,702 outputs
Outputs of similar age from Integrated Environmental Assessment & Management
#11
of 14 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 975 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one is in the 32nd percentile – i.e., 32% 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 416,702 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.