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Replicating the microbial community and water quality performance of full-scale slow sand filters in laboratory-scale filters

Overview of attention for article published in Water Research, September 2014
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

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9 tweeters

Citations

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31 Dimensions

Readers on

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120 Mendeley
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Title
Replicating the microbial community and water quality performance of full-scale slow sand filters in laboratory-scale filters
Published in
Water Research, September 2014
DOI 10.1016/j.watres.2014.05.008
Pubmed ID
Authors

Sarah-Jane Haig, Christopher Quince, Robert L. Davies, Caetano C. Dorea, Gavin Collins

Abstract

Previous laboratory-scale studies to characterise the functional microbial ecology of slow sand filters have suffered from methodological limitations that could compromise their relevance to full-scale systems. Therefore, to ascertain if laboratory-scale slow sand filters (L-SSFs) can replicate the microbial community and water quality production of industrially operated full-scale slow sand filters (I-SSFs), eight cylindrical L-SSFs were constructed and were used to treat water from the same source as the I-SSFs. Half of the L-SSFs sand beds were composed of sterilized sand (sterile) from the industrial filters and the other half with sand taken directly from the same industrial filter (non-sterile). All filters were operated for 10 weeks, with the microbial community and water quality parameters sampled and analysed weekly. To characterize the microbial community phyla-specific qPCR assays and 454 pyrosequencing of the 16S rRNA gene were used in conjunction with an array of statistical techniques. The results demonstrate that it is possible to mimic both the water quality production and the structure of the microbial community of full-scale filters in the laboratory - at all levels of taxonomic classification except OTU - thus allowing comparison of LSSF experiments with full-scale units. Further, it was found that the sand type composing the filter bed (non-sterile or sterile), the water quality produced, the age of the filters and the depth of sand samples were all significant factors in explaining observed differences in the structure of the microbial consortia. This study is the first to the authors' knowledge that demonstrates that scaled-down slow sand filters can accurately reproduce the water quality and microbial consortia of full-scale slow sand filters.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 120 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
United Kingdom 2 2%
Indonesia 1 <1%
Chile 1 <1%
Germany 1 <1%
Colombia 1 <1%
Unknown 112 93%

Demographic breakdown

Readers by professional status Count As %
Student > Master 33 28%
Student > Ph. D. Student 32 27%
Student > Bachelor 13 11%
Researcher 10 8%
Other 5 4%
Other 11 9%
Unknown 16 13%
Readers by discipline Count As %
Environmental Science 31 26%
Engineering 25 21%
Agricultural and Biological Sciences 17 14%
Biochemistry, Genetics and Molecular Biology 7 6%
Earth and Planetary Sciences 3 3%
Other 11 9%
Unknown 26 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 07 November 2017.
All research outputs
#2,015,387
of 12,109,122 outputs
Outputs from Water Research
#494
of 5,486 outputs
Outputs of similar age
#35,144
of 196,953 outputs
Outputs of similar age from Water Research
#3
of 46 outputs
Altmetric has tracked 12,109,122 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,486 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done particularly well, scoring higher than 90% 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 196,953 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.