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A-DROP: A predictive model for the formation of oil particle aggregates (OPAs)

Overview of attention for article published in Marine Pollution Bulletin, March 2016
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  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

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Citations

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Title
A-DROP: A predictive model for the formation of oil particle aggregates (OPAs)
Published in
Marine Pollution Bulletin, March 2016
DOI 10.1016/j.marpolbul.2016.02.057
Pubmed ID
Authors

Lin Zhao, Michel C. Boufadel, Xiaolong Geng, Kenneth Lee, Thomas King, Brian Robinson, Faith Fitzpatrick

Abstract

Oil-particle interactions play a major role in removal of free oil from the water column. We present a new conceptual-numerical model, A-DROP, to predict oil amount trapped in oil-particle aggregates. A new conceptual formulation of oil-particle coagulation efficiency is introduced to account for the effects of oil stabilization by particles, particle hydrophobicity, and oil-particle size ratio on OPA formation. A-DROP was able to closely reproduce the oil trapping efficiency reported in experimental studies. The model was then used to simulate the OPA formation in a typical nearshore environment. Modeling results indicate that the increase of particle concentration in the swash zone would speed up the oil-particle interaction process; but the oil amount trapped in OPAs did not correspond to the increase of particle concentration. The developed A-DROP model could become an important tool in understanding the natural removal of oil and developing oil spill countermeasures by means of oil-particle aggregation.

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X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 2%
Unknown 53 98%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 10 19%
Student > Ph. D. Student 9 17%
Other 4 7%
Researcher 4 7%
Professor 4 7%
Other 7 13%
Unknown 16 30%
Readers by discipline Count As %
Engineering 16 30%
Environmental Science 7 13%
Chemical Engineering 5 9%
Earth and Planetary Sciences 2 4%
Unspecified 1 2%
Other 5 9%
Unknown 18 33%
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 28 April 2016.
All research outputs
#19,945,185
of 25,374,917 outputs
Outputs from Marine Pollution Bulletin
#6,369
of 9,589 outputs
Outputs of similar age
#218,454
of 314,267 outputs
Outputs of similar age from Marine Pollution Bulletin
#84
of 207 outputs
Altmetric has tracked 25,374,917 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 9,589 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.4. This one is in the 29th percentile – i.e., 29% 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 314,267 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 207 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 52% of its contemporaries.